Your background is in electronics. How did you find your way into software testing and automation?

To be honest, I never set out to become an automation expert! After college, I joined Infosys as a software developer, working mainly with SOAP services. While I was supposed to be building features, I realised I was more interested in finding what could break. My teammates often teased me, saying I tested code more than I wrote it.

Over time, I saw how my engineering mindset analytical, logical, and system focused gave me a different lens to approach software. A senior colleague once introduced me to automation, and everything clicked. It was the perfect mix of coding and quality. That’s when I knew I’d found my niche.

You moved from development into testing quite early. How did that shape your growth?

That change happened early in my career, and looking back, I’m really glad it did especially when I moved to the US. Having a background in both development and testing gave me more room to explore and grow. I was able to write code while understanding the bigger picture of automation.

Over the years, I’ve worked on everything from analysing requirements to writing detailed test cases for web apps and improving existing frameworks. Eventually, I got to lead automation strategies for web and API testing. With tech evolving so fast, automation engineers have to keep learning constantly. It’s become a habit for me. I’m currently pursuing certifications in DevSecOps and API Security Automation to stay on top of new trends.

You began your journey with Infosys, one of India’s top IT companies. How did that shape you?

Infosys was a great place to start. I joined as a Java developer, and that’s where I really learnt to write clean, structured code. But it wasn’t just about coding the company culture taught us discipline, client communication, and the importance of delivering value.

One of the things I appreciated most was the focus on understanding the end user and business impact. That approach still guides me today. Whether I’m testing software or leading a project, I always try to think from the user’s point of view.

Today, your focus is workflow automation. What drew you to this area?

I started working on QA automation around 2009. Since then, I’ve kept up with new tools and practices, which helped me build solid frameworks. At Uline, I began focusing more on workflow automation finding ways to simplify complicated processes across teams.

With the help of modern tools, I’ve been able to boost efficiency and connect automation directly to our CI/CD pipelines. That hands on work taught me a lot about DevOps too. I’m also working on ways to add security checks within these pipelines to guard against cyber threats. The goal is always to evolve with the tech and make sure software is fast, safe, and dependable.

How has software testing changed since you first started?

When I began, testing was something we did at the end if there was time! Now, it’s part of the whole development cycle. One big shift has been the move to “left shift” testing, where quality checks begin early and developers take more ownership.

Back then, setting up test environments used to take hours. Today, thanks to automation, we can spin them up in seconds. The tools may have changed, but the mission is still the same: ensure the product works and protect the user experience by catching problems early.

You’ve recently been named a Senior Member of IEEE. What has that community meant to you?

Being part of IEEE has been really valuable. It’s given me access to a network of professionals from all kinds of industries, which helps me learn and stay open to different ideas. The Software Quality group, in particular, has been a great place to share knowledge that applies across sectors.

Becoming a Senior Member felt special it made me reflect on the journey from writing basic scripts to now leading full automation projects. I also want to give back, and mentoring younger engineers through IEEE is one way I’m doing that.

What would you say to young engineers in India who hope to build global careers?

Stay rooted in your technical foundations that’s your superpower. Indian education builds strong problem solving skills, and those really stand out globally. But remember, tools and tech will keep evolving. What matters most is how quickly you can learn and adapt.

Also, don’t underestimate the power of communication. For a long time, I thought being good at tech was enough. But being able to explain your ideas clearly can open so many doors. Stay curious, take on challenges outside your comfort zone, and don’t wait to be perfect just be ready to grow.

And one such person who has been fighting to pave his way through every storm is Raj Kundra, a British-Indian businessman and an incredible actor. He was ranked as 198th richest British Asian by Success. He was also among the promoters of – Best Deal TV, an online and TV broadcast platform capitalizing on celebrity endorsements. At the same time, his involvement in social change has been commendable. He was awarded with ‘Champions of Change Award’ by Pranab Mukherjee, the former President of India for his involvement with Swachh Bharat Mission. But there’s more to him than meets the eye.

His journey didn’t begin with a silver spoon. He transformed his humble beginnings into glorious success. He was born in a middle-class family where his father was a bus conductor in London and mother worked as a shop assistant. It was at the age of 18 when Kundra decided to get into the business of selling pashmina shawls in Britain and that was the beginning of his life as an entrepreneur. Eventually in 2007, he moved to Dubai and set up a business that dealt with precious metals, mining and renewable energy projects. And not just that, he also had his foot inside the entertainment industry where he financed and produced Bollywood films.

But life has its way of throwing challenges our way. Kundra’s journey was no different. His life got upside down too. He was arrested in July 2021 and spent two months in jail. Eventually in September 2021, he was released on bail. But those 2 months changed his life forever. He faced humiliation, the media was behind his wife, children and parents and he himself was in a trap from which he felt like there was no way out. But just like the protagonist of every film, he didn’t let the setbacks hold him for longer.

“I decided to not only come out of the real-time prison, but also the prison made by the society. I knew that I can’t let things happen and I must take matters into my own hands. So I got back. For myself, for my wife, my children, my parents and for every person who has been in my shoes,” says Raj Kundra. Through this hard journey, Kundra embraced strength and resilience reminding us that life never ends. There’s always a way. Very soon, Raj Kundra wrote and starred in his film – UT69. The film gives us a glimpse into the 63 days he spent inside India’s most crowded jail – Arthur Road Jail. It not only showcases the hardships endured by Kundra but also gives us a glimpse of life of jail inmates and throws light on the situations they live under.

Raj Kundra went beyond his own experience and also has another film lined up for release this year – Mehar. It’s directed by Rakesh Mehta, and produced by Divvya Bhatnagar and Raghu Khanna. It’s going to be released on September 5th, 2025 and is already in the watchlist of so many cinebuffs. It’s a heartfelt family drama that also revolves around love, friendship and life.

Throwing light on his life, Raj Kundra says, “People are always going to say things. Wrong stuff will always come your way. But you always have the chance to rewrite your setbacks into comebacks. One of the lines I always remember during these times is by Maya Angelou:

“You may write me down in history,
with your bitter, twisted lies.
You may trod me in the very dirt.
But still, like dust, I’ll rise.”

Raj Kundra’s story of strength reminds us of the Phoenix inside us. It might burn down, but from its ashes, it always comes back to life.

Disclaimer: Consumer Connect Initiative

In this blog, you’ll learn how food and sleep are connected, and what simple changes can help you sleep better and feel more rested.

The Hidden Connection Between Food and Sleep

Here’s the surprising link between what’s on your plate and how well you sleep at night.

Your Diet Shapes Your Sleep Quality

You might be surprised to know that your diet plays a big role in how well you sleep. Clive Gray, from London Review of Suit Tailors, shares, “Many entrepreneurs struggle with rest but don’t realize their food choices might be the reason.”

The link between what you eat and how you sleep is stronger than you think. Some foods help you relax, while others keep your brain too active.

In fact, a study found that people who eat more fiber and less fat tend to sleep more soundly and enjoy deeper, better-quality rest. It’s a small shift that can make a big difference — especially when your brain is always in work mode.

The Right Nutrients for Restful Sleep

If you want better sleep, your body needs the right fuel. Nutrients like magnesium, melatonin, and healthy fats help you relax and sleep deeper. Magnesium — found in things like almonds, spinach, and bananas — helps your body feel calm.

Martin Seeley, CEO & Senior Sleep Expert at Mattress Next Day, explains, “Melatonin is the hormone that controls your sleep, and certain foods like cherries and oats can boost it. Even healthy fats from nuts or fish help your body settle down.”

But if your diet is full of sugar, caffeine, and packaged snacks, sleep gets harder. These things mess with your system, make your mind too active, and often lead to waking up in the middle of the night.

If you’re waking up tired, your food might be part of the problem.

Timing Matters

When you eat matters just as much as what you eat. Eating heavy meals late at night can make it harder to sleep. Your body stays busy trying to digest the food instead of relaxing. You may feel full, bloated, or just too uncomfortable to rest properly.

Kim Lewellen, Attorney of Lewellen Family Law Group, mentions, “Skipping meals during the day causes problems. It can mess with your energy and make it hard to settle down at night. Try to eat on time and avoid going to bed right after dinner. If you get hungry later, have something light like a banana or some yogurt.”

Common Eating Habits That Disrupt Sleep

Here are common eating habits that disrupt your sleep.

Drinking Too Much Coffee Late in the Day

Coffee gives you that boost when you’re tired, but having it too late can mess with your sleep, even if you don’t feel it right away. Caffeine stays in your system for several hours.

Ushmana Rai, Founder of TDEECalculator.me, adds, “If you’re having coffee in the late afternoon or evening, it can keep your brain too active when it’s time to rest. You might fall asleep later than usual or wake up more during the night.”

Try having your last cup before 2 PM — especially on days when you already feel a bit wired or stressed.

Eating Large or Heavy Meals at Night

A big dinner right before bed can leave you feeling full, heavy, and uncomfortable. Your body has to focus on digesting the food instead of relaxing. This makes it harder to wind down and can lead to disturbed sleep, bloating, or even heartburn.

“If you often feel sluggish in the morning or like you didn’t sleep well, your dinner timing and portion size might be part of the issue. Try to have your last big meal at least two to three hours before bed, and keep it lighter — like grilled chicken with veggies or a bowl of soup,” says Per Markus Åkerlund, CEO of MEONUTRITION.

Too Much Sugar During the Day

It’s easy to reach for sweet snacks when you’re low on energy — especially during long workdays. But sugar causes quick spikes and crashes in your blood sugar. That crash later in the day can leave your body feeling stressed or anxious, which makes it harder to fall asleep.

It also messes with hormones that help you rest. Even healthy-sounding snacks like granola bars or “energy” drinks are often loaded with hidden sugar. So just eat meals with a balance of protein, fiber, and good fats to keep your energy steady.

Skipping Meals or Eating at Random Times

Missing meals or eating at odd hours throws your body out of sync. Your body works better on a routine — especially when it comes to eating and sleeping.

Davin Eberhardt, Owner of Grow Eat and Repeat, says, “When your meals are all over the place, it can confuse your internal clock. This makes it harder for your body to know when to feel alert and when to wind down.”

A study published in PLOS ONE found that later meal timings, including the first, midpoint, and last meals, were associated with poorer sleep quality. Try to keep your meals spaced out evenly through the day — it helps more than you think.

Late-Night Snacking

Many people snack late at night just because they’re bored, watching TV, or stressed — not because they’re actually hungry. This can become a routine, and your body starts expecting food when it should be resting.

According to Sumeer Kaur, Founder of Indian Clothes, “Late-night snacks, especially ones high in sugar or salt, can disturb your sleep without you realizing it. If you’re truly hungry, go for something small and calming like a banana or a warm glass of milk.”

But if it’s just a habit, try replacing it with something else that helps you unwind — like a short walk, a shower, or just turning off screens a bit earlier.

The Best Foods for Better Sleep

Here are the best foods you can try for better sleep.

Bananas

Bananas are one of the easiest sleep-friendly snacks you can have. They’re packed with magnesium and potassium, which help relax your muscles and calm your body Jenn Denfield, Marketing Director of Emergenetics International, advises, “Bananas contain tryptophan, an amino acid that helps your body make serotonin and melatonin — two key chemicals for sleep. Eating a banana an hour before bed can help your body feel more relaxed and ready to rest.” Plus, they’re light on the stomach, so you won’t feel too full. Whether eaten plain or added to a warm cup of milk, bananas are a great go-to for a better night’s sleep.

Cherries

Jake Emmanuel, Business Owner & CEO of Trees By Jake explains, “Cherries — especially tart cherries — are a natural source of melatonin, the hormone that controls your sleep cycle. Drinking a small glass of tart cherry juice or eating a handful of cherries in the evening may help you fall asleep faster and stay asleep longer.”

Some studies even show cherry juice can improve sleep quality in people with insomnia. They’re also rich in antioxidants, which help your body fight stress and inflammation — two things that often mess with sleep.

Just make sure you don’t eat too many at once, especially if they’re dried or sweetened. Fresh or frozen is best.

Oats

Oats aren’t just for breakfast — they’re also great for bedtime. A small bowl of warm oatmeal at night can help your body produce melatonin naturally. Bryan Dornan, Founder of Second Mortgage Rates, shares, “Oats contain complex carbs that help tryptophan reach your brain faster, which help you feel sleepy sooner.”

They also keep your blood sugar stable through the night, which prevents those early-morning wakeups that come from sudden energy drops. Add a little milk, banana, or honey for an extra sleep boost. Just keep portions small, so it’s not too heavy.

Almonds

Almonds are rich in magnesium, which plays a big role in calming your nervous system. This helps reduce stress and relax your muscles — two things that are important for falling asleep easily. Just a small handful is enough.

Steve Caya, Wisconsin Personal Injury Lawyer at Nowlan Personal Injury Law, notes, “Almonds have protein and healthy fats, which keep you feeling full without feeling heavy. That helps stop late-night hunger that might otherwise wake you up.”

You can eat them as-is or spread almond butter on a slice of whole-grain toast for a more filling snack. Just avoid the salty or flavored ones, since added sugar or salt can ruin the effect.

Warm Milk

There’s a reason warm milk is such a classic bedtime drink. It contains tryptophan, which helps your body produce serotonin and melatonin. But it’s not just the nutrients.

The warmth itself can have a calming effect, especially when sipped slowly. Warm milk can help signal to your brain that it’s time to wind down.

For some people, it also brings a sense of comfort or routine, which makes it easier to fall asleep. You can add a pinch of cinnamon or nutmeg if you like, but keep it simple.

The Long-Term Benefits of Sleep-Focused Nutrition

In reality, there are many benefits of sleep-focused nutrition. And the list will not end if we explain them. But here are some long-term and important benefits.

More Energy and Focus in Daily Business Tasks

You’ll have more energy to get through your workday if you get better sleep. You wake up feeling refreshed when your body receives the proper nourishment and relaxation. No more using coffee to remain awake or experiencing mid-day collapses. You do more in less time and maintain mental acuity.

Improved Mood, Creativity, and Decision-Making

Sleep and food affect your mood. Lack of sleep leads to stress, irritability, and brain fog. But when you nourish your body with the right foods, your brain functions at its best. Good sleep improves creativity, problem-solving, and decision-making — all crucial for running a business.

Sustained Success

Working smarter is the key to long-term success. Entrepreneurs that look after their health are more productive, have clearer thinking, and manage stress better.

Your productivity and general well-being increase when you place a high priority on eating healthy and getting enough sleep. In addition to improving your everyday performance, you’re positioning yourself for a healthier and more prosperous future by implementing incremental, long-lasting adjustments.

Stronger Immune System and Fewer Sick Days

Your immune system is directly impacted by sleep and diet. Your body repairs cells and fights off infections as you sleep. Also, you are providing your body with the resources it needs to remain strong when you feed it meals high in vitamins, such as zinc, vitamin C, and vitamin D, explains Armstrong Lazenby, Founder of Fitness Image.

Eventually, this means you’ll be less susceptible to colds and stress-related fatigue. Also, when you do become sick, you’ll recover quickly. Better attendance at crucial meetings, more consistent work, and less interference with day-to-day or company operations are all benefits of taking fewer sick days.

Balanced Hormones and Better Weight Control

Ghrelin (hunger hormone), leptin (fullness hormone), insulin (blood sugar), and cortisol (stress hormone) are all greatly affected by your sleep patterns and diet. Excessive eating, sugar cravings, and stress are more likely to occur when they are out of balance.

But these hormones remain in check when you eat healthily and receive regular rest. Longer feelings of fullness, less comfort food cravings, and easier weight management are all results.

This will ultimately end up in improved digestion, fewer energy dumps, and a more consistent body weight — all of which will increase your self-assurance, concentration, and control.

Wrap Up

Finally, let’s end the talk of how food and sleep impact your performance. Because it’s time to take action. You don’t have to start changing everything at once. Of course, it’s not possible. So just start choosing better meals, stay hydrated, and set a consistent sleep schedule.

Also, track how these adjustments affect your energy and focus.

This will help you think more clearly, work smarter, and feel better every day. So don’t wait for the new year to set the goal — just start now!

Developed by Alma Lasers, the Alma Duo utilizes a patented low-intensity extracorporeal shockwave therapy (LI-ESWT) technology to improve blood flow and restore natural function. The treatment protocol comprises six treatments lasting 15 minutes each over three weeks, with no side effects and no downtime. $500 is charged for every session of Alma Duo Shockwave therapy, which sets the expected cost for the six-session treatment program to be somewhere in the area of $3,000. According to Mr. Lior Dayan, the Chief Executive Officer of Alma, The launch of Alma Duo is yet another demonstration of how we are leading the way in developing innovative technologies that enable medical practitioners to offer real, effective solutions to the problems faced by their patients, thereby improving the quality of life for everyone.”

Erecticare Pro is a product of a wondrous combination of two outstanding doctors, A.K. Jain and Sankalp Jain, who had a marvelous innovation without a needle, Viagra, or radiation involved in the therapy of erectile dysfunction.

 It has changed the lives of many thousands of people across India. Dr. Sankalp Jain, who is part of Erecticare Pro, spoke in an interview, which further stated that the medical technology would be revolutionized for the sake of men, to enjoy their sexual life by improving their blood flow up to 95 percent naturally. “With clinical evidence, Erecticare Pro seems to be a true solution for ED due to its benefits such as increase penile blood flow, noninvasive method, long-lasting results, etc.” The price per Erecticare Pro therapy session ranges from $250 to $500, while a total six-session treatment plan will cost somewhere between $1500 and $3000.

With this partnership, both brands aim to bring a unique approach to men’s sexual wellness and health, focusing more on guided protocols for treatment, coaching, and expert consultations. Together with Alma Duo’s clinical precision, this collaboration sets a new standard for erectile wellness in India.

The joint project will initially be made available in selected locations in Uttar Pradesh, with eventual plans to expand in other metro cities. Patients can benefit a lot from streamlined sessions and non-invasive treatment methods. With an increase in awareness around sexual health of men and a growing demand for ED treatments and medication, this partnership couldn’t have come at a better time. Together, both collaborators are not only providing treatments but also changing lives as we know it.

For more information on Erecticare Pro, visit https://askdrjain.in/erecticarepro/.  

The incident re-enforced his conviction that compliance and performance share the same bloodstream. Months earlier, a central-bank rule barred locally issued – locally acquired transactions from crossing national borders. Tejas devised a dynamic routing engine that quarantines sensitive data in on-shore enclaves while preserving global fallback paths, marrying sprint-level agility with line-item audit rigor. That pattern now circulates across the issuer’s engineering guild as the de-facto standard for jurisdiction-aware payment logic, cementing his reputation as an engineer who reads statutes as fluently as packet captures.​“My experience of implementing large scale contactless fare systems has taught me that reliability hinges on simple, testable code. Consistent performance earns commuter trust without fanfare.”

Peers describe his code reviews as a triangulation exercise—metrics, profiler traces, and business KPIs must all agree before a line ships. The discipline pays off in Net Promoter Scores that have inched upward even as transaction volumes expanded double digits. To Tejas, craftsmanship is not an indulgence but the entry fee for operating at national scale, and real success arrives in the quiet moments when customers forget the infrastructure exists at all.

Engineering Automation That Frees Minds and Pipelines

If payment networks are Tejas’s stage, automation is his lighting rig—the invisible scaffolding that lets performers focus on art. In 2023 he led a JDK uplift spanning twenty-three microservices for a leading mutual-insurance group. Rather than brute-force refactors, his team built a continuous-delivery pipeline that scanned reflection-based calls, surfaced deprecated APIs, and queued pull requests at the exact lines demanding change. Disposable Kubernetes jobs ran integration tests on every commit, and the switchover landed with zero customer downtime. The framework—now marketed internally as an “evergreen pipeline”—saves the firm roughly 1,200 engineering hours each release cycle.​

Earlier, as a consultant to a consumer-credit platform, he attacked eight-hour regression suites that crippled delivery cadence. By fanning Cucumber tags across parallel runners and provisioning short-lived agents in the cloud, test time fell to three hours, returning an entire sprint of developer capacity to ideation. The philosophy followed him to his current post, where nightly canary deployments trial fraud-rule updates against live traffic partitions so defects surface while the blast radius is microscopic. “Having led multi-branch pipeline automation, I have seen firsthand that the best scripts fade into the background. They let engineers focus on invention rather than infrastructure.”
Tejas rejects dashboards that deliver vanity metrics, insisting every alert map to a remediation playbook. In sprint retrospectives he will spike a metric if no one can describe how it protects customer journeys. That rigor aligns DevOps spend with business impact and keeps engineering energy pointed at the frontier, not consumed by the machinery meant to liberate it.

Extending Innovation from Transit Gates to Cloud Containers

Innovation, for Tejas, is portable. In 2019 he turned near-field communication into subway convenience with a tap-to-ride enhancement that decoupled gate speed from real-time authorization, allowing riders to cross turnstiles in milliseconds while back-end reconciliation finalized fares asynchronously. The design proved that thoughtful separation of concerns could reshape human experience—a lesson he later applied to message-driven micro-architectures using Kafka topics and RabbitMQ queues.​​

That same year he produced a proof-of-concept branch-cleanup tool for a sluggish Jenkins instance at the same issuer. Scoring stale branches by last-commit date and merge status, the tool reclaimed gigabytes of memory and revived CI/CD dashboards for hundreds of engineers. By 2024 he had introduced a suite of external mock services on OpenShift, replicating third-party endpoints that once bottlenecked integration tests across eighteen squads. Release trains that formerly skipped cycles now depart on schedule, an outcome he attributes to empowering teams to test assumptions early rather than bartering for lab time later.

The common thread is empathy for delay-sensitive users—subway riders who will not wait and API callers whose SLAs tolerate no drift. Each solution turns latent friction into optionality: performance headroom, schedule slack, or developer cognitive space. Collectively these gains compound into a strategic moat, converting technical excellence into enduring customer loyalty.

Building Culture Through Mentorship and Collaborative Review

Tejas’s influence travels farther through people than code. Weekly defect-triage sessions feel like master classes: junior developers replay incident timelines, while senior staff debate whether pattern-matching belongs in gateways or domain layers. The ritual transforms outages into institutional curriculum, ensuring vacations pose little risk because collective knowledge, not tribal memory, drives on-call rotations.

His hiring bar favours curiosity over surface-level gloss. Candidates who ask incisive framing questions earn fast-track callbacks; once onboard, engineers rotate repository stewardship so no single person guards arcane build scripts. Documentation becomes a survival trait, and operational fluency spreads organically—a boon on Friday evenings when a customer-support ping escalates into a Sev-2 alert. “Throughout years translating statutes into code, I have learned that algorithms must never outrun the guardrails that protect customers and institutions. Transparency is the real accelerator of innovation.”

Beyond process, he cultivates psychological safety. Pull-request comments centre on trade-offs, and retrospectives open with a rule: blame belongs to systems, not people. New hires adopt senior habits within weeks, compressing the time it takes for fresh engineers to ship production-grade code. In an industry where talent churn can erode velocity overnight, that culture may be his most durable architecture.

Anticipating the AI-Driven Future of Compliance and Fraud Prevention

Ask Tejas about the frontier and he speaks of feature stores, edge inference, and data-sovereignty zones with equal fluency. Today, anomaly-detection models embedded in gateway proxies score every request against multidimensional baselines and trip circuit breakers before latency breaches service objectives. Retraining occurs nightly on sanitized transaction shards to minimize drift while honouring privacy statutes.​​

His ambitions stretch further. He is prototyping federated-learning agents that train on-premise, exchange gradients across data centres, and update parameter servers in ways that satisfy the strictest residency laws. Coupled with reinforcement-learning policies that tune cryptogram thresholds in real time, the architecture promises fraud prevention that scales linearly with transaction complexity without multiplying false positives. Regulators, he notes, will demand algorithmic explainability; accordingly, the platform logs every feature vector alongside its downstream decision, allowing compliance officers to replay inferences during tabletop drills. The transparency compresses certification timelines and keeps product launches synchronized with market windows instead of legal negotiations.

If the past decade was about moving compliance from overnight batches to per-transaction verdicts, the next will push that cadence to millisecond horizons. Tejas’s roadmap fuses edge computing, AI as control plane, and zero-trust data zoning into a single thesis: the future of payments belongs to networks that convert complexity into predictability without sacrificing privacy or speed.

Guiding Trust into Every Transaction

From subway turnstiles to cloud clusters to cryptogram checkers, Tejas Dhanorkar has shown that resilient systems depend less on glamorous algorithms than on disciplined engineering decisions made early and revisited often. His career illustrates a reliable pattern: identify latent friction, automate it away, and reinvest the dividends in customer value. The payoff is a network that transforms a swipe into an unspoken promise—one kept quietly, swiftly, and unfailingly, every single time.

He begins each engagement with an “evidence sweep”: CPU saturation curves, queue depths, garbage-collection pauses, and patch-level drift across hybrid estates. These metrics are codified into Terraform and Ansible so the baseline can be reproduced in minutes. With observability, security, and rollback traveling alongside business logic, inevitable surprises manifest as clear, actionable signals rather than cryptic stack traces. As deployments shift from monoliths to containers, Vijaya’s guard-rails move seamlessly—blue-green templates, health probes, and admission-controller policies embed operational wisdom into every YAML file. “My experience of implementing large-scale container platforms has taught me that reliability is engineered long before the first pod starts. Clear policies and consistent observability make scaling a routine, not a rescue,” he notes. The outcome is unambiguous: change-failure rates decline, audit findings shrink, and infrastructure spending ties directly to product velocity rather than emergency overtime.

Foundations of Reliability

Vijaya’s respect for first principles was forged during nights dissecting heap dumps and SSL handshakes. Those sessions revealed that most outages begin as faint anomalies: a cache miss that stretches a response by fractions of a millisecond, or a thread pool that never fully drains after a processing surge. By converting such weak signals into concrete metrics, he transforms intuition into automation. At the insurer mentioned above, TLS configuration, queue-depth thresholds, and JVM parameters became version-controlled artifacts. Each pull request triggered quality-gates that validated performance budgets and encryption posture; the same dashboards satisfied security analysts and auditors, removing the traditional divide between development and compliance.

Modernization projects follow a similarly deliberate rhythm. Before a workload is containerized, Vijaya “characterizes” it over several release cycles—capturing thread-dump signatures, database fan-in patterns, and latency histograms. Migration scripts appear only after this evidence review, unfolding in incremental stages that conclude with blue-green cutovers. Operational knowledge, thus embedded, allows a rollback to become a simple label switch rather than a high-stress intervention. “Over decades spent refining middleware foundations, I learned that guard-rails widen roads instead of narrowing them. When baselines are peer-reviewed code, freedom and confidence scale together,” he reflects.

Governance follows the same pattern of frictionless enforcement. Policy-as-code engines intercept non-compliant images long before production, yet developers self-serve fixes by updating the very manifests that failed validation. Vijaya insists that every failure path educate rather than punish; the platform is largely invisible until it must speak, and then it does so in the objective language of actionable telemetry.

Automating Trust at Enterprise Scale

A premier US payments network provided a definitive test of Vijaya’s rigor: 120 interdependent services required cloud modernization under intense regulatory oversight. He began not with architecture diagrams but with a Terraform module encapsulating segmentation rules, encryption defaults, and cost-allocation tags. From that seed grew a disciplined ecosystem: infrastructure changes entered exclusively by Git pull requests, SonarQube gates enforced code hygiene, and AppDynamics fed live performance heat maps directly into sprint retrospectives. Within half a year, deployment lead-time fell from multiple release cycles to a single iteration, while change-failure rate dropped nearly forty percent—figures confirmed by the organization’s risk committee.

Central to this acceleration is Vijaya’s “deployment health score,” a composite index blending test coverage, latency budgets, and vulnerability scan results. Displayed simultaneously on engineering monitors and executive dashboards, the score transforms disagreements into data-driven decisions. Security specialists contribute policy updates through the same Git workflow as feature developers; auditors shift from periodic freezes to perpetual attestation; release managers realize that postponement adds no safety once the score is green. “Lessons accumulated while modernizing regulated platforms showed me that transparency is the most effective compliance strategy. Shared metrics replace theatrical risk meetings with routine, evidence-based planning,” Vijaya explains.

Guard-rails, however, remain collaborative. Pipeline templates are open to revision, rollback hooks publish human-readable remediation steps, and every auto-revert tag includes guidance for re-promotion after a fix. This shared stewardship rewires organizational culture: developers treat latency anomalies as solvable puzzles rather than finger-pointing episodes, and executives link infrastructure budgets to observable product gains instead of unverified assurances. Trust becomes the platform’s most scalable feature.

Cultivating Continuous Improvement

Technical discipline sustains performance metrics; human dynamics sustain progress. Vijaya therefore embeds psychological safety into the delivery process. Fortnightly “architecture cafés” invite junior engineers to present anomalies such as packet-loss blips or thread-starvation events while senior architects practice active listening. Whiteboard sessions conclude with action items that feed directly into the next sprint backlog. Six months into one such program, engagement surveys showed a double-digit rise in employees who felt comfortable admitting mistakes—a signal matched by an equally strong uptick in proactive pull requests focused on operational resilience.

Incident management exhibits the same empathy-infused structure. Sev-1 bridges open with a timeline of facts, hypotheses, and next experiments. Annotated post-mortems become searchable artifacts feeding capacity-planning models and onboarding curricula. Rotating on-call ownership broadens domain knowledge, while recognition of small wins—an extra unit test here, a tighter readiness probe there—compounds into more resilient code bases. As developers internalize a culture where evidence beats ego, release cadence accelerates without sacrificing stability.

Budget committees notice the shift: automation investments correlate with lower rework costs and higher customer satisfaction scores. Retention charts improve as night-time alerts decline, validating Vijaya’s belief that robust platforms and healthy teams reinforce one another rather than compete for attention.

Engineering for an AI-Powered Landscape

With guard-rails and culture firmly in place, Vijaya now pursues predictive operations. He pilots large-language-model prompts that transform plain-language governance—“encrypt all customer data in transit and at rest”—into Open Policy Agent rules automatically inserted into CI pipelines. Initial results compress compliance rollout from several iterations to a handful of days and identify configuration drift within hours. Parallel telemetry pipelines feed multivariate anomaly detectors that correlate Kubernetes events, database latencies, and CI telemetry. Pilot clusters have already flagged the majority of high-severity incidents at least two hours early, granting engineers the luxury of graceful remediation instead of crisis intervention.

Reinforcement agents further refine autoscaling thresholds based on historic diurnal patterns, trimming compute expense while maintaining latency objectives. Chat-ops bots assemble incident updates by summarizing Grafana traces, linking them to the pull requests that introduced regressions, and proposing rollbacks with a single click. Vijaya cautions that algorithms amplify existing discipline rather than substitute for it, yet within ecosystems where every log line and policy rule is version-controlled, AI becomes a force multiplier of human insight.

His roadmap envisions predictive insights as routine, compliance expressed conversationally, and engineering creativity redirected toward higher-order abstractions such as user experience and domain modelling. By preserving clean seams—data, policy, and remediation exposed as stable APIs—Vijaya ensures that machine reasoning integrates naturally instead of via brittle glue code.

A Vision Anchored in Quiet Excellence

Across continents and industries, Vijaya’s blueprint remains consistent: automate the obvious, illuminate the unknown, and keep users blissfully unaware of the machinery beneath. The platforms he architects are praised not for flash but for the silence they foster—silence that signals predictability. Operational expenditures track downward because waste is measurable and pruned; release trains accelerate because guard-rails eliminate hesitation; auditors close findings swiftly because evidence is innate, not after-the-fact. A “quiet platform,” in Vijaya’s lexicon, does not hide problems—it surfaces only the right ones to the right people at the right moment.

As enterprises accelerate toward an AI-shaped horizon, Vijaya’s principle—evidence first, empathy always—offers a pragmatic compass. His career demonstrates that resilience and velocity are not opposing endpoints but co-products of disciplined automation and human-centric culture. By weaving those values into every line of code and every team ritual, Vijaya Bhaskara Rao ensures that the future of cloud will continue to speak in a language of calm, measurable confidence

Cloud maturity turns theory into practice in his refactor of a corporate‑ and retail‑car‑reservation platform for a leading automotive manufacturer, where he decomposed monoliths into AWS Lambda micro‑services fenced by authorizers and web‑application firewalls. DynamoDB tables negotiate their own write capacity in real time; Step Functions coordinate overnight reconciliations without blocking daytime transactions; and Jenkins pipelines shepherd commits from GitHub through SonarQube quality gates to production in under an hour. The result is velocity without volatility—feature squads deploy weekly, and administrators modify inventory or fee structures with zero downtime. “My experience of implementing large‑scale reservation platforms has taught me that every service boundary is a promise of independence. Honor that promise, and traffic surges feel perfectly ordinary.”

Modular Mastery for Relentless Scale

Radhakrishnan Pachyappan sharpened his composable mindset while rescuing Inspire—a deal‑modeling engine a global consulting giant depends on for client pricing—from legacy bottlenecks. Stateless Lambdas fronted by API Gateway replaced brittle servers, S3 events refreshed master data automatically, and Code Pipeline compressed release cycles from days to hours, slicing overall processing time by 60 percent. What had once required overnight batch jobs now completes before a coffee break, proving that serverless is not merely cost‑efficient—it is decisively faster.

Modularity extends beyond code to culture. Weekly “failure rehearsals” throttle staging environments, compelling runbooks to demonstrate their mettle under synthetic stress while SonarQube blocks merges that exceed agreed cyclomatic‑complexity thresholds. Dashboards map every pull request to deployment frequency, tying individual behavior to business cadence in real time. Governance therefore shifts from a periodic inspection to an always‑on feedback loop, turning quality into a shared gamified objective. “Implementing cloud transformations at a broad scale has shown me that cost savings endure only when latency budgets and compliance rules are welded into sprint zero—retrofitting discipline is the costliest refactor of all.”

Industry Velocity Across Domains

Radhakrishnan Pachyappan draws on manufacturing floors, trading desks, and underwriting models to guide design reviews that respect each sector’s non‑negotiables. Sensors on assembly lines demand millisecond telemetry or risk idle machinery; finance mandates deterministic audit trails for every state change; insurance models rely on unbroken lineage across datasets to ward off risk exposure. When he led fifteen specialists on the automotive fleet initiative, he paired Flutter for mobile and Angular for web, sharing TypeScript logic without sacrificing native idioms. Terraform declared every environment, while Artifactory pinned container images to guarantee reproducibility.

Mapping technical debt to unit economics, he shows product owners why a deferred refactor costs multiples downstream. Concurrency ceilings translate into currency terms, lending architecture meetings the same clarity as balance‑sheet reviews. That business fluency secures a budget for resilience features—rate limiters, circuit breakers, blue‑green deploys—that technical teams often struggle to prioritize.

Consistent delivery metrics emerge: on‑time launches, uptime north of 99.99 percent, and change‑failure rates beneath industry benchmarks. Stakeholders learn to equate architectural rigor with predictable revenue, anchoring the notion that robust systems are not overhead but strategic advantage.

Leadership Mentorship and Culture

Radhakrishnan Pachyappan treats mentorship as a multiplier, hosting office hours where engineers defend design trade‑offs in plain language rather than jargon. Pull‑request reviews home in on naming clarity and algorithmic intent with equal vigor, because self‑documenting code accelerates future velocity. Rotating on‑call leadership ensures every team member feels the weight—and therefore the value—of operational excellence.

He seeds psychological safety by framing failures as learning artifacts. Post‑incident reviews ask, “What telemetry would have flagged this sooner?” rather than “Who missed the signal?” That shift converts war‑room anxiety into design‑backlog prioritization, embedding reliability into the next iteration instead of burying it in a blame ledger. “In guiding multi‑industry digital initiatives I have learned that culture is architecture’s invisible load‑balancer. When teams feel safe to surface risk early, systems remain safe to carry the business later.”

Governance, Compliance, and Observability

Radhakrishnan Pachyappan insists that observability must be as integral as business logic, embedding distributed traces, structured logs, and real‑time metrics from sprint zero. He treats dashboards as shared contracts: if an SLA matters, there is a gauge; if a threshold can break budget, there is a redline alarm. Automated canaries run after every infrastructure change, capturing latency deltas before users do.

Compliance shifts from annual audits to living documentation. Infrastructure‑as‑code doubles as evidence; change histories link git commits to incident tickets; and policy‑as‑code tools assert encryption, data residency, and retention rules at deploy time. Auditors no longer sift through binders—they scroll dashboards with time‑based diffs.

When anomalies slip through, sampling pipelines fork suspicious traffic into secure sandboxes for forensic replay. Root‑cause findings feed back into Step Function branches, closing the governance loop so that the next violation is pre‑empted. This virtuous cycle keeps regulators satisfied and keeps operations focused on innovation rather than remediation.

AI as the Emerging Design Partner

Radhakrishnan Pachyappan is piloting reinforcement agents that tune DynamoDB capacity units and tighten WAF rulesets in near real time, trimming hot‑partition incidents by double digits during holiday spikes ​. Security payload inspectors now spin up sandbox replicas the moment anomalous fields appear, turning quarterly audits into continuous compliance streams. These AI teammates surface anomalies and propose remedies before alerts reach human operators, nudging architects from reactive firefighting toward proactive mentoring.

Development workflows evolve in parallel. Junior developers push code while AI bots annotate concurrency footprints and suggest shard keys, leaving senior engineers to validate domain heuristics rather than syntax minutiae. The resulting feedback loop compresses onboarding curves and produces infrastructure that effectively learns its own limits.

Radhakrishnan’s next experiment integrates UML‑driven behavior diagrams with generative policy engines, compiling governance rules directly from design artifacts. In that future, architects curate principles, and AI enforces them at runtime—closing the loop between intent and execution.

Navigating the Next Horizon

Radhakrishnan Pachyappan measures success by the silence of well‑behaved systems: when capacity doubles and dashboards stay green, stewardship trumps spectacle. He charts a five‑year vision blending event streaming, supervised learning, and self‑healing pipelines, each observable from a single pane that even a day‑one hire can navigate. In markets chasing novelty, his compass aims for maintainability, clarity, and intelligence advancing together.

Leadership, he believes, is succession planning in disguise. By turning architectural principles into shared muscle memory, he ensures that platforms and people will outlast his own tenure. As organizations stride into AI‑augmented cloud landscapes, the blueprint he offers—disciplined, observable, culture‑forward—remains the surest route to innovation without compromise.

My experience of implementing large‑scale Azure tenant security build‑outs has taught me that a disciplined baseline outlasts any single tool. Baselines draw bright lines that empower teams to innovate without wondering where the edge lies.” 

Colleagues now invite security to backlog‑grooming sessions, not war‑room scrambles. Templates supplant guesses; metrics trump anecdotes; evidence overshadows opinion. As Senior Security Engineer for a healthcare‑data innovator, he insists that every feature ship with a one‑page threat model, automated control tests, and a residual‑risk tile that sits beside performance indicators—proving that secure‑by‑design can be fast‑by‑design, too. 

Engineering Zero Trust at Enterprise Scale 

Feroskhan inherited a newborn Azure tenant: diagnostic logs off, permissive network rules, and sprawling admin identities. He cataloged every identity, workload, and data flow, then overlaid Conditional Access, multifactor authentication, and Privileged Identity Management. Microsoft Defender for Cloud flagged misconfigurations; private endpoints and deny‑by‑default firewalls carved a segmented topology; Sentinel stitched logs into a single narrative for analysts and auditors. Weekly threat‑model workshops turned policy mandates into engineering puzzles, converting skeptics into co‑designers of Zero Trust patterns and spawning a purple‑team playbook library mapped to MITRE ATT&CK. “Drawing from years of architecting high‑stakes cloud environments, I remind teams that Zero Trust is not mistrust—it’s verified confidence. Two sentences of prevention avert hours of incident‑response improvisation.” 

Nightly drift scans compare every subscription against source‑of‑truth templates and open tickets for any deviation; PowerShell‑driven quarterly reviews force role owners to re‑justify privileges or watch them expire. Six months in, auditors noted a 92 percent drop in critical findings, and release leads trimmed deployment timelines by a third with hardened IaC modules. The Print Nightmare drill validated the playbook mindset: exposure verified, mitigations deployed, and regulator evidence filed before COB—proof that rehearsed muscle memory beats heroic improvisation. 

Elevating Endpoint Defense 

Feroskhan treats every laptop as a strategic asset because endpoints remain attackers’ favorite beachhead. Intune baselines enforce BitLocker, Secure Boot, and Credential Guard on Windows; JAMF mirrors those guardrails on macOS. CyberArk Endpoint Privilege Management eliminates standing local‑admin rights, granting elevation only through expiring approvals captured in Sentinel. Defender for Endpoint funnels behavioral telemetry into analytics that compress thousands of raw events into a dozen high‑fidelity alerts each morning. 

A spear‑phishing macro last fall evaded email filters, but device isolation kicked in within sixty seconds, blocking outbound traffic and snapshotting volatile memory. Automox patched the fleet inside forty‑eight hours, slashing mean remediation from fifteen days to under seventy‑two hours. Context‑rich prompts now tell users why an action is blocked and how to request elevation, cutting help‑desk friction by 27 percent and proving that transparent friction drives adoption. “From orchestrating countless cross‑domain incident drills, I can attest that empowered teams respond faster and recover cleaner. Culture—not tooling—decides whether a control becomes muscle memory or shelf‑ware.” 

Monthly “endpoint game‑days” inject simulated ransomware and credential‑harvesting scenarios, measuring both detection speed and communication clarity across operations, legal, and customer support. Findings feed directly into policy updates, closing gaps long before real adversaries can exploit them. 

Identity as the New Perimeter
Feroskhan regards identity as the single immutable control surface in a cloud where networks dissolve into micro‑services. My first act on any green‑field project is a Security Design Review that forces teams to diagram every caller, scope, and secret before the first pull‑request merges. Tokens are issued through OIDC or OAuth 2 and stored in Azure Key Vault under hourly rotation jobs; no secret ever lives longer than its ticket. RBAC assignments mirror separation‑of‑duties boundaries approved by compliance, while Conditional Access and Privileged Identity Management (PIM) keep administrative paths behind multi‑factor gates and time‑boxed elevations. Because policy alone can drift, I wire real‑time signals—Azure AD sign‑in logs, Defender for Cloud identity alerts, and PIM elevation feeds—into Sentinel where KQL rules flag dormant accounts, unused roles, and high‑risk consent grants. 

The results have been measurable. Within six months of rollout, privileged‑access sprawl fell 38 percent and audit cycle time halved because the evidence auditors used to assemble manually now lives in dashboards refreshed every ten minutes. A dormant test identity that suddenly authenticated from an overseas IP was disabled before lateral movement because its unusual geography triggered both a risk‑based Conditional Access rule and a Sentinel anomaly. Engineering teams, initially wary of just‑in‑time elevation, now see it as licence‑request relief: they borrow rights for the exact duration of a task instead of waiting weeks for permanent roles. Even service principals are held to account—nightly jobs compare their Graph scopes against a “least‑privilege catalogue” and open pull‑requests that down‑scope permissions automatically when drift appears. 

Mathematics alone cannot retire redundant rights; diplomacy finishes the task. I facilitate monthly “scope clarity” reviews where architects walk through token flows and blast‑radius maps. When engineers see that tighter scopes can speed pipeline duration and shrink incident coverage windows, they rarely cling to stale privileges. Upcoming enhancements push identity hygiene even closer to design time: Visual Studio Code extensions will highlight overly broad Graph scopes inside source, and an LLM‑powered assistant will draft least‑privilege alternatives alongside developers. My goal is identity surfaces auditors call narrow, transparent, and provably defensible—without ever slowing a release train. 

Cultivating Distributed Custodianship
Feroskhan believes an enterprise survives not by the sophistication of its tooling but by the vigilance of its people. I rotate junior developers through four‑week “security sprints” where they build threat models, write Sentinel queries, and pair with blue‑team analysts on live investigations. The experience demystifies risk and turns abstract controls into personal accomplishments. Live fire‑drills follow: credential‑theft, data‑exfiltration, and supply‑chain scenarios run on production‑parity sandboxes, scoring not just mean‑time‑to‑detect but clarity of cross‑team communication. Post‑exercise surveys now show a 31 percent rise in employees who feel “confident” handling incidents, and proactive misconfiguration tickets outnumber auditor findings three to one. 

The cultural heartbeat is Threat Horizon, a digest I publish on the first Monday of each month. Written in plain English, it distills public advisories into a two‑page narrative that maps likely exploit paths against our stack, ranks business impact, and links to backlog items engineers can pull that very sprint. Executives skim the heat‑map sidebar for board updates; product owners drop the linked user‑stories straight into planning; customer success teams convert the “What this means for clients” section into talking points that reassure stakeholders. The newsletter consistently tops internal engagement charts, driving a virtuous loop: higher readership begets earlier questions, which surface blind spots before an adversary can. 

During the Log4Shell crisis the payoff was unmistakable. Because the December edition had already flagged recursive JNDI lookups as an emergent risk, platform squads possessed upgrade branches, rollback plans, and test harnesses days before the public CVE. Patches hit production inside forty‑eight hours—well ahead of industry medians—and the crisis became a showcase in quarterly business reviews. Leadership subsequently green‑lit a “Security Makers” guild: a community of practice that seeds every scrum team with at least one contributor trained to write IaC guardrails, extend Sentinel detections, and coach peers. Distributed custodianship shifted security’s image from gatekeeper to accelerator and unlocked budget for deeper automation and AI pilots. 

Harnessing AI for Continuous Verification
Feroskhan treats artificial intelligence as an amplifier that can widen defenders’ field of view without surrendering judgment. I run a trio of LLM‑driven micro‑services. The first summarizes Sentinel incident clusters into 60‑second analyst briefs that combine notebook screenshots with “most‑probable kill‑chain step” predictions. The second agent ingests raw Defender for Endpoint telemetry, then groups low‑signal anomalies by behavioral similarity to collapse alert volume by 80 percent. The third hooks into Azure Repos pull‑requests, reading IaC diffs and drafting least‑privilege alternatives when it spots broad role assignments. All outputs remain in a human‑in‑the‑loop queue until precision exceeds 95 percent, and every prompt—complete with context tokens—is logged to a private audit index to satisfy privacy regulators. 

Governance is not optional. Training corpora exclude customer identifiers, and model artifacts ride through a secure MLOps pipeline that signs, scans, and quarantines weights if supply‑chain tampering is suspected. Quarterly “AI red‑team” events pit synthetic attack sequences against the models to gauge recall drift: if detection confidence falls below threshold on new malware families, retraining fires automatically with curated samples. We also measure time‑to‑first‑intelligence—the minutes between a public CVE drop and the model’s first accurate classification—to prove that automation outpaces threat‑actor tooling. In the last fiscal year the metric improved by 27 percent. 

Future milestones push AI from observability into design enforcement. A permission‑scope simulator, now in alpha, predicts blast radius before code merges by injecting proposed role assignments into a digital twin of the tenant graph. Concurrently, a regulation parser built atop GPT‑4o ingests updated frameworks—SOC 2, HIPAA, EU AI Act—and converts clauses into BDD‑style acceptance tests. Developers see failing tests during CI runs rather than in post‑audit findings. By moving from periodic attestation to continuous verification, we expect to retire 40 percent of manual control checks and reinvest that analyst time into proactive threat hunting—reinforcing the principle that AI should elevate human focus, not replace it. 

Security as Innovation’s Guardrail
Feroskhan frames security for executives as an insurer of strategic optionality: resilient systems let a business pivot quickly without renegotiating trust every cycle. Quarterly board reports therefore foreground dwell‑time compression and deploy‑with‑confidence rates alongside EBITDA, demonstrating that robust defenses shorten sales‑cycle legal reviews and accelerate regulatory approvals. When a prospective healthcare partner asked for proof of HIPAA alignment, dashboards exported from compliance APIs provided line‑item evidence in hours, not weeks, shaving a full quarter off contract closure. 

Financial signals tell the same story. Post‑breach forensic fees are down 60 percent year‑over‑year, cyber‑insurance premiums dropped after reinsurers saw live control metrics, and cloud cost overruns fell because hard tenancy boundaries prevent shadow resources. Equally important is reputational capital: customer NPS surveys list “trust” as a top‑three reason for renewals, and marketing teams now pitch secure‑by‑design as a brand pillar. On the investor front, quarterly earnings calls feature a security slide that links uptime SLAs and low incident‑impact scores directly to revenue retention, translating technical diligence into Wall Street language. 

The North Star remains unchanged: Assume Breach • Verify Everything • Empower Everyone. Every dashboard, purple‑team exercise, and AI policy ladders to that compass. Over the next twelve months I will embed SLSA provenance attestation into artifact pipelines, publish an open‑data “defensive transparency report,” and pilot confidential compute for PII tokenization. Each initiative reduces blast radius, boosts audit readiness, or accelerates feature release. In an industry racing toward GenAI‑driven products and multi‑cloud federations, the greatest differentiator is the ability to ship fast without gambling on safety. My journey—from crowded server rooms to board presentations—proves that when security is treated as design work, not damage control, it becomes the silent engine of durable progress. 

The platform’s backbone emerged from Bhaskar’s conviction that lineage must travel with every row. A Python validation framework performs schema drift checks before data reaches downstream marts; failed records detour to a quarantine route, preserving analytic integrity without slowing the happy path. Even compliance auditors—once tethered to protracted lineage hunts—trace a guest’s profile from kiosk swipe to marketing blast in fewer than ten clicks. The immediacy has reshaped decision cycles: revenue teams hold “lunch-and-launch” huddles that swap static weekly reports for real-time dashboards, and property managers experiment with room-upgrade incentives that react to occupancy swell in under thirty minutes.

“My experience of implementing large-scale data pipelines is that clarity in customer intent dictates every design choice, and once the ‘why’ is visible, the ‘how’ aligns naturally,” Bhaskar notes, underscoring that his architecture privileges business context over tooling hype.​​

Powering Instant Decisions in Financial Services

Long before casino floors and resort towers, Bhaskar refined his craft inside a global payments institution struggling with fraud-screening latency. Legacy MapR clusters digested card-swipe logs overnight, leaving investigators to chase stale anomalies. Bhaskar replaced the batch monolith with Spark Structured Streaming, partitioned on issuer geography and card family, then emitted micro-batches every three seconds to a rules-engine sandbox. Incident-response windows compressed from hours to minutes; false-positive escalations dropped by 22 percent; and the institution reclaimed seven figures in dispute fees during the first quarter post-cutover.​​

Equally transformative was his decision to lay observability rails before performance tuning. He instrumented Kafka topic lag, executor memory churn, and rule-evaluation latency in Grafana, letting SREs correlate user complaints with pipeline health in real time. That transparency shifted the culture from reactive war-room scrambles to proactive capacity planning: peak holiday shopping now triggers automated autoscaling rather than frantic hardware requisitions.

“Implementing enterprise streaming taught me that you can’t optimize what you can’t witness,” he tells colleagues. “Sustainable speed emerges only when telemetry, not adrenaline, guides the throttle.”

Engineering Cloud-Native Scalability Without Sacrificing Certainty

Bhaskar’s cloud playbook rejects blanket migration mantras in favour of selective control. In the CDP, transient Spark pools absorb promotional surges, yet deterministic SQL pools guard finance aggregates whose quarter-close deadlines brook no jitter. For legacy Teradata marts, he orchestrated phased lifts: first replicating tables to Delta Lake, then throttling dual-writes until cost-to-serve validated the switchover. The outcome: a 28 percent infrastructure-spend reduction and 40 percent faster analytics refresh, achieved without a single missed service-level objective.​​

Key to that success is Bhaskar’s “blue-green schema” principle. Every breaking change spawns a parallel dataset with full lineage tags, letting analysts A/B test queries while pipelines stabilize. Confluence runbooks codify cut-over timing, rollback triggers, and owner sign-offs—documents that slash onboarding time for new engineers and expand institutional memory.

“Years of leading cloud migrations convinced me that governance is not overhead; it is the receipt that lets leadership spend an insight with confidence,” he remarks, framing compliance as a value multiplier rather than a speed bump.

Building Governance and Trust into Every Byte

In Bhaskar’s view, data quality is inseparable from business credibility. His Python validation suite—deployed across finance, loyalty, and operations marts—executes column-level null-rate thresholds, referential integrity checks, and PII redaction before write-ahead logs commit. Alerting hooks into Microsoft Teams channel anomalies to domain owners, turning data stewardship from a back-office chore into a shared muscle.​​

He extends that ethos to security. Field-level encryption keys rotate via Azure Key Vault every 30 minutes; privilege boundaries mirror the company’s zero-trust macros; and access reviews auto-generate diff-reports for identity teams. When a hospitality subsidiary requested GDPR alignment, Bhaskar’s lineage mapping shaved the remediation estimate by half because sensitive fields were already traceable to their ingress points.

Finally, he threads ethical guardrails into machine-learning workflows. Model cards declare data provenance, training drift metrics, and fairness audits. Deployment pipelines block models lacking bias attestations, preventing “shadow AI” from reaching production. That diligence not only appeases regulators but also lends the brand a transparency halo coveted in the loyalty space.

Mentoring Engineers and Business Stakeholders Alike

Bhaskar multiplies impact by elevating those around him. A 12-week rotation cycles new hires through ingestion, transformation, and visualization squads; shadow commits mature into lead features by week nine. Attrition among graduates hovers below five percent, a stat HR attributes to his scaffolded learning path.​​

Yet mentorship extends beyond engineers. Product owners attend “data-design studios” where white-boarded user stories receive lineage annotations in real time. Finance analysts preview SQL plans before rollout, ensuring metric alignment. And quarterly “pipeline retros” invite marketing, risk, and compliance to grade data freshness, defect rates, and analytic adoption—an exercise that has lifted cross-team Net Promoter Scores by 18 points in two years.

The payoff surfaces during audits and performance reviews alike. Engineers present stack decisions in business vernacular, while executives reference lineage dashboards confidently in board decks. The cultural dividend: data ceases to be an IT asset and becomes a lingua franca that knits departments together.

AI-Driven Horizons: Closing the Loop from Creation to Consequence

Looking forward, Bhaskar envisions large-language-model copilots absorbing descriptive analytics so humans can hunt causal insight. He is already piloting a retrieval-augmented generation layer that condenses campaign metrics into prose directly inside the CRM, cutting weekly review prep from four hours to fifteen minutes.​​

Beneath that convenience sits a rigorous ethics stack. Differential privacy guards low-cardinality segments; synthetic data augments sparse classes; and bias scans flank model re-training. The coming wave, Bhaskar predicts, is vector databases that store behavioural embeddings alongside transactional facts, powering room-upgrade nudges the moment a guest slows at a lobby kiosk. But precision must never outpace permission: consent flags propagate through Kafka headers, ensuring downstream models honour opt-outs instantly.

“Scaling machine learning has shown me that relevance without trust is thin ice; only when transparency walks in lockstep with automation does AI create durable value,” Bhaskar contends, summarizing a philosophy that weds innovation to accountability.

MetaTwin forms the cornerstone of this revolution through its generative AI platform, which creates lifelike AI twins during real-time video and audio synthesis. Synthetic media personas developed through Kumar’s low-latency ML models will operate the new creator economy delivering tutoring, coaching services, and interactive content at scale.

A Technical Architect of Modern AI Systems

It is this synergy of technical proficiency with visionary thought that separates S. Avinash Kumar from others in this generative AI journey. Avinash Kumar worked at Core ML in applied research at Meta Platforms, where he built data-centric AI systems that earned hundreds of millions in annual revenue impact. His foray into driving his vision into reality was at Lyft, where Kumar built fundamental ML algorithms and models that drove self-driving innovation; Amazon and Google Cloud ML were also built around the AI and AI-infused business model.

He patented 16 inventions, including six already approved patents, covering self-driving vehicle algorithms and AI-generated AI twins. He published above ten academic papers, besides writing four books that formed the cornerstone of various scholarly works pertaining to ethical ML systems and generative AI. Kumar remains very active in academia by chairing sessions in some major AI events while he also serves as a member in different editorial boards of journals.

MetaTwin: Building Future of Synthetic Media

MetaTwin Inc. allows Kumar to make avenues of expected capabilities synthetic media come real with its product platform. Content creators, educators, and now even professionals and end-users, have spaces that allow them to create AI twins, as termed, or selected personas handling via a digital platform. Real-time AI twins deployed on multiple audience-facing platforms deliver scalable user experiences.

MetaTwin functions with exceptionally innovative generative AI models and AI systems. A system developed by Kumar delivers AI twins at low latency using AI models and training paradigms that require small amounts of data. The combination lowers entry barriers while decreasing costs to make this technology accessible to a wider range of stakeholders.

Real-World Impact: Monetization and Engagement

Measurable evidence of Kumar’s innovative work has already manifested. The early users of MetaTwin’s AI twins produce $30,000 worth of monthly revenue through their offerings that include engaging simulations, language tutoring, mentoring, and business coaching. AI-based twins create deeper engagement with users than standard video presentations because they drive engagement and retention rates up according to benchmarking analytics and client witnessing.

Ethics At The Core of Innovation

Although the technology is potent, Kumar is acutely aware of the ethics of synthetic media. Under his guidance, MetaTwin introduces digital watermarking, consent frameworks, and AI-based identity verification to foster transparency and accountability.  “Our mission isn’t simply to make synthetic personas feasible,” says Kumar, “but safe and aligned with human values.”

Ethical AI represents an essential feature of Kumar’s executive leadership.

Shaping What Comes Next

Kumar plans to spend resources on creating highly immersive simulations that allow users to engage with the AI twin in the digital world. Powered by this advancement, synthetic media and the digital world will become more accessible to everyone through a process matching social media sign-up procedures.

Through his vision, S. Avinash Kumar sees digital clones providing services to multiple industries such as education, coaching, and business mentorship. According to Kumar, the scope of techniques only depends on the extent of human ethical design principles and creativity boundaries.

Conclusion

The work of S. Avinash Kumar consists of much more than technology development because he constructs a novel paradigm. Through his contributions, Kumar is developing AI replicas that combine ethical attributes with scalability and monetization potential, which determine interactions between humans and machines and between humans and the digital world. Through visionary leadership and an ethical foundation he creates a blueprint that shows how AI will be equally powerful and intelligent.

The innovations of Kumar will be recognized for their founding role in three ways: learning, communication and work, since synthetic media will become fundamental to how humans operate in digital spaces.

“How do you make something invisible, like performance engineering, the backbone of an industry that thrives on visibility?” Avinash Swaminathan Vaidyanathan asks. This question actually reflects a good amount of his career, spanning 15 years through the whole gamut of performance engineering and site reliability engineering, culminating in his current role.

From ensuring that self-checkout systems do not fail during holiday rushes to optimizing cloud-based retail platforms for speed and reliability, Avinash Swaminathan Vaidyanathan’s expertise is quietly improving the way retail operates.

Engineering Excellence in a Changing Industry

From generative AI (genAI) to omnichannel integration, technology is reshaping every facet of the shopping experience. For Avinash Swaminathan Vaidyanathan, this transformation is both a challenge and an opportunity. “Retailers today are not just selling products; they’re selling experiences,” he explains, “and those experiences are built on systems that need to be fast, reliable, and scalable.”

Using his background in performance engineering and site reliability engineering (SRE), he implemented advanced solutions designed to stress-test systems under simulated failures. “It’s about building resiliency,” he notes. You don’t just want systems that work; you want systems that thrive under pressure.”

The Human Guiding the Technology

Despite his technical proficiency, Avinash Swaminathan Vaidyanathan still believes in the importance of collaboration and mentorship in his work. “Performance engineering isn’t just about tools and metrics; it’s about people,” he says. At Dollar General, he leads a team of engineers to ensure the company’s digital platforms remain robust and efficient, adopting agile methodologies to streamline workflows and foster innovation.

Avinash Swaminathan Vaidyanathan’s influence also resonates across the broader retail sector. As companies deal with challenges like economic uncertainty and shifting consumer expectations, it is not uncommon for others to borrow a page from Avinash’s playbook to use it as a blueprint for leveraging technology to take on these complexities. Whether optimizing supply chains or enhancing customer personalization through AI-driven insights, his contributions are helping retailers adapt to an increasingly digital world.

More to Do for Retail Innovation

Avinash Swaminathan Vaidyanathan remains focused on pushing the boundaries of what performance engineering can achieve, as he continues to see immense potential in emerging technologies like genAI and advanced analytics. “These tools are game-changers,” he says, “they allow us to predict issues before they occur and deliver experiences that feel almost magical to consumers.”

Despite being far ahead of his time, Avinash Swaminathan Vaidyanathan remains grounded in the fundamentals of his craft: precision, reliability, and a drive for improvement. “At the end of the day,” he closes, “performance engineering is about making things better—not just for businesses but for people.”

Use Disclaimer- Consumer connect initiative

Why Passive Investing Makes Sense

Unlike active investment, the main goal of passive investment is not beating the market. Rather, it follows a benchmark index and is focused on the index and no longer tracking individual securities and timing the market. Passive investing has the following benefits:

• Lower cost: The absence of a fund manager’s role in executing trades on an ongoing basis implies low cost for the fund.
• Simplicity: You can have the advantage of knowing the destination of your investment exactly which is an index which is the subject of public scrutiny and easy to read.
• Long-term consistency: Markets in the long run tend to go up and index funds are what enable you to ride on that wave of high tide.
• Fewer emotional distractions: Lower trading and decision-making make it simpler to stay focused when the market is volatile.

Passive investing is a part of global personal finance these days, so the next step is learning how to properly plan your investments.

What Is an MF Calculator?

An MF calculator is an online calculator that assists you in estimating the future value of your mutual fund investment. It takes away the guesswork and enables you to make a plan using actual figures, not guesses.
Few of the MF calculator types are:

• SIP Calculator: Assists you in determining how much the monthly investments can become after a given period.
• Lumpsum Calculator: Enables you to compute the maturity value of a single investment.
• Goal-Based Planner: Let you know how much you have to pay per month or a lump sum amount so that you can achieve a particular goal in a specific timeframe.

By playing with variables such as amount invested, time to hold, and assumed rate of return, you can see how your index fund investments work.

Why Index Mutual Funds and Calculators Work So Well Together

Index funds are ideal for long-term objectives. But for everyone, the challenge is not to invest—it’s how much, and for how long. A mutual fund calculator provides the solution precisely.

For example, let us consider that you need to invest ₹20 lakhs over a tenure of 15 years so as to pay the educational fees of your child. Taking the speculated 11% return, you can know with the SIP calculator that you will have to make around ₹4,500 each month. Now, you have a simple, pragmatic plan ahead of you and not some vague notion.

When you put together the predictability of an index fund with the planning power of a calculator, you create a system in which you can place a value on consistency more than frequent decisions.

Step-by-Step: Index Fund Investment Planning with an MF Calculator

Step 1: Define Your Goal
Choose what you are saving for—retirement, a home, or a long-term financial goal. Be specific about how much you require and how much time you have.

Step 2: Choose Your Investment Type

Do you want to invest each month via SIP, or invest a lumpsum? With a fixed income, the SIP is the best answer for most situations.

Step 3: Utilize the MF Calculator

Enter the amount you want to invest, the time period, and finally, enter the expected return. For example, most of the index mutual funds in India have provided 10-12% returns on a year-on-year basis over a period of time.

Step 4: Analyze the Output

If the calculator indicates that you will not be able to save the entire amount, just increase your monthly SIP or extend the investment period. Keep changing the figures until you align with your goals.

Step 5: Be Consistent and Stay on Track

Once you have your plan set, get yourself a good index fund, and then set up your SIP. All done, then remember to stick to your choice and keep away from the market maelstrom for some time.

Popular Index Funds in India to Try

If you are going to make use of an MF calculator for creating an index-based portfolio, you can opt for schemes such as:

• Nifty 50 Index Fund
• Sensex Index Fund
• Nifty Next 50 Index Fund
• Nifty 100 Equal Weight Fund
• Nifty Midcap 150 Index Fund

These are offered by various AMCs (Asset Management Companies), and although the portfolio is identical, expense ratios and tracking differences can vary. Choose one with low expense ratio and minimal tracking difference.

Real-World Example: Building Wealth with Simplicity

Amit, a 32-year-old engineer, wishes to retire at the age of 55. He believes he will require ₹1.2 crore. He is a first-time investor and does not wish to invest time in studying stocks.

By using an MF calculator, he calculates that investing ₹10,000 per month in an index mutual fund for 23 years with a return of 11% per annum will help him achieve his dream.

He selects a low-cost Nifty 50 index fund, initiates a SIP on his MF platform, and arranges for a monthly automatic debit from his bank account. With no effort whatsoever and without any need to monitor the markets, Amit is now headed for a safe early retirement.

Mistakes to Avoid When Planning with MF Calculators

While calculators are useful, keep the following in mind:

• Don’t expect fixed returns: Markets change. Make use of conservative estimates of returns (e.g., 10–11%) for index funds.
• Don’t underestimate inflation: If your target is ₹20 lakhs in present value terms, account for inflation and shoot higher.
•  Don’t over-customize: Use standard, wide-market index funds for consistency.

Also, revisit your plan every year. If your income increases, raise your SIP. If you’ve received a bonus, consider topping up your index fund investment.

When to Recalculate or Rebalance

You don’t need to go over your index fund or calculator every week, but it’s also wise to check your plan:
Annually, to factor in pay raises or life events

• After significant life events, like marriage, home purchase, or having a child
• When market conditions change dramatically, necessitating a shift in expectations of return

The goal isn’t to keep making adjustments, but to stay on guard and in control.

Final Thoughts

Portfolio planning doesn’t have to be complicated. Using an MF calculator and the stability of index mutual funds, you can create a passive investment plan that’s based on reason and designed for long-term success. It’s not timing the market or choosing the next big stock. It’s about consistency, clarity, and commitment. Begin small, plan wisely, and see your wealth grow—one monthly SIP at a time.

Disclaimer- Consumer connect initiative.

First published date 5th July 2024

A well-known leader in technology innovation, Sachin Dixit puts him in a place to use his skill in AI and Machine Learning for the transformation of the Fintech sector. He has close to two decades of work experience and majorly worked on AI, fintech, data engineering and enterprise application. Sachin combines high technical prowess with strategy accrued from the heart of innovation, San Francisco Bay Area. He is postgraduate from IIT-Kharagpur, Masters in Computer science, NPU and specialisation in Machine Learning from UC Santa Cruz which more significantly add both technically and with respect to precedent. 

During this time in his career, he has had integral responsibilities in implementing high-value solutions across leading organizations, such as Stripe Inc., Yahoo Inc., Oracle Corporation, and Fidelity Investments. Some of his award-winning achievements include best team player at Stripe, star employee at Yahoo, and spot awards across the other organizations. His professional ties with the Institution of Engineers (India) serve to signify his commitment towards technology and engineering.

He is a member of the Emerging Leaders committee, involved in Oracle Applications and Technology User Group (OATUG). A Senior Member of the IEEE, he is on a number of committees, including Technical Community on Data Engineering, Technical Community on Cloud Computing, and Technical Community on Security and Privacy, and others. Sachin is a member of SigmaXI, an organization with a mission to promote excellence and meaningful advancements in the technology area. 

It, in effect, qualitatively confers free patent rights onto him in this domain. The patent specifies that the technique involves usage of AI technology in hoisting process performance from an AI and Enterprise perspective.

Sachin records for Solutions Architecture at Stripe and specifically Venue Engineering for almost all projects that remain so contemporary with respect to financial systems. He has another major role in the redesigning of a financial data warehouse by bringing fragmented systems together, along the title – Lead Project Champion.

He had driven the innovation project in the year 2023 using the latest technology stack with the awesome features offered by AWS, Airflow for workflow management, Spark, Scala, Python, and Golang with back S3 storage and querying all done by Trino.Implemented integrated custom batch-processing models for data engineering, as well as Kafka for real-time data solutions and personalized adapters for third-party applications and legacy systems. Security governance should be emphasized more with restricting access controls per limited access among several teams.

An initiative was undertaken to create a common data warehousing system with the intention of achieving operational cost savings, process optimization, and capabilities for real-time reporting. It laid the foundation for collaborating on data while independently drawing insights of importance to all actors in the financial realm. It will serve to add further insight into business planning by CEOs, and its flexibility will never deter Stripe from growing with respect to stability or security.

Sachin’s quite the unsung value in the company, where every task he undertakes anchors the system, which then carries out in an utmost efficient way, saving millions while keeping that efficiency running with such high traffic. The program really is built on solid ground, and from here, everything else falls into place along trustworthy AI applications, predictive analytics, large-scale data processing, and auditing processes. So, Sachin keeps making Stripe leading in a fast-changing AI-Driven Fintech ecosystem. 

Well, Sachin keeps leading Stripe in a fast-evolving AI-driven fintech ecosystem. Among all the accolades and ranks he has earned during his corporate career, he has also managed to publish a few research articles in even more highly-sought-after academic arenas as evidenced from TechRxiv (IEEE), JISEM, and IJSRSET. The publication of the article “AI Powered Risk Modeling in Quantum Finance: A New Paradigm for Enterprise Decisions Systems” in July 2022 has managed to raise eyebrows only with the title.

The paper abstracts how very likely are the unimaginable fusion between artificial intelligence and quantum computing to modify the manner of financial risk modeling and enterprise decision-making occur. These ranges can vary from hybrid algorithms outperforming purely classical algorithms in precision, performance, and scalability to the applications such as financial model optimization or fraud detection.

In every possible way, Sachin’s leadership is beyond the technical achievements of his race. He creates an environment that holds bold ideas, mentoring engineers to link their work with business objectives, thereby incorporating security design ideals. His reach is global: judging tech competitions at Conrad and Globee Awards; speaking at ISMG events and IEEE conferences; sharing expertise through white papers on AI and security at such platforms as TechRxiv; member of Funds Grant committee at Sigma XI GIAR program, “The more I learn, the more I share” adds an ever-expanding, crescent cycle of growth and innovation to a sustainable influence. With regard to his future, Sachin is considering agentive tools for AI, cloud-centered architecture, and process automation to augment engineering capabilities. He visualizes proactive real-time systems that would anticipate problems with AI to reduce cost-acquired overhead while enhancing performance. An innovator in his own domain, Sachin Dixit is a true man of past, present, and future with a tech lens so powerful that its view will change the future for AI and Fintech. In this way, his work will give visibility to a different kind of progress, while mentorship to today will provide a foundation for tomorrow’s innovators.

A grand event organised in the presence of Union Commerce Minister Shri Piyush Goyal, Paytm’s Vijay Shekhar Sharma, and boAt’s Aman Gupta, the conversations led to the high-growth startups and unicorn stories. Rather, it made space for the lesser-known, but no less substantial, role of India’s 63 million MSMEs, enterprises that make up the backbone of the country’s economy, and employ more than 110 million individuals.

During his address, Minister Goyal made a pointed observation: India’s economic ambitions cannot be realised without actively supporting its MSME sector. He spoke of the need for practical support through funding access, policy clarity, and easing compliance for the millions of small enterprises that quietly power the nation’s growth.

In a moment that many in the audience took to heart, a Certificate of Appreciation was handed to Ahmedabad-based Egniol, a platform that has been steadily working with MSMEs over the years. From guiding businesses through government and private growth schemes to supporting them with compliance, funding assistance, and long-term strategy, the firm has become a trusted partner to thousands of entrepreneurs across India, clearly building the MSME ECOSYSTEM with the help their Two public-interest platforms,i.e., MSME Samvaad and My MSME House. Those are reliable resources for business owners seeking clarity, updates, and guidance that reaches one lakh businesses subscribed and these initiatives play a significant role in driving MSME growth.

The festival also showed the changing ways in which MSMEs are adapting to challenges. With growing regulatory requirements and the need to stay informed, digital platforms are increasingly becoming essential tools for small business owners. They offer one place to track policy changes, apply for schemes, and manage day-to-day requirements, freeing up entrepreneurs to focus on what they do best: run their businesses.

While the Vejalpur Startup Festival remains an arena for fresh ideas and innovation, this year’s iteration marked a marked change. The focus shifted, if only for a moment, towards the hundreds of millions of small businesses working behind the curtain, and towards the individuals and platforms they rely on. In doing so, the festival quietly reset its definition of success in India’s business scene.

The event did what most startup festivals try to accomplish: energizing founders and bringing about dialogue. What made it different was that it took time to stop and acknowledge those that don’t tend to get put in the limelight.

Cooking was learned by Thrushna at the tender age of five, which sparked her lifelong interest in the art. The Culinary Arts remained a cherished insight and hobby for her. Little did she realize the dynamics of that profession would inadvertently hinder her potential as an Artist in Cuisine! Then, on turning 26, Thrushna realized she could really turn her hobby into a career; with several leaps of faith armed by determination, grit, and phenomenal talent!

Thrushna’s beginnings in the culinary world were in Hyderabad, India, wherein she worked in kitchens ranging from traditional to modern, with severely high volumes to uphold the rich regional flavors. Dubai was where she expanded her horizons in bakery and pastry, nurturing a precision that would later become the hallmark of her aesthetic. The experience gained at 800Thali and Hummingbird Bakery gave her both macro and micro perspectives-scale and finesse, two crucial traits in professional kitchens. K.C. Gargkalz.

Eventually, her extended pursuit of technical mastery took her to the Culinary Institute of America, where classical techniques and modern gastronomy were her subjects of immersion. Since then, she has worked her magic in various prestigious American kitchens, such as Salamander Resort and Boulud Sud. In each kitchen, she has honed her skills-a twisting relationship between beauty and discipline-whether by managing staff through service, up to the plating station, or designing stunningly stand-out dishes. Today, her work supports some of the most imaginative and pressure-filled functions under the Jean-Georges banner-each one buoyed by the wonderful world experience, artistry, and exacting standards she brings to every service.

We sat down with Chef Thrushna to capture her ever-expanding habitat at the Tin Building, along with her experience at the Family Reunion Festival at Salamander Resort, and discuss the importance of finesse, discipline, and cultural storytelling in her craft.

Let us start with the Tin Building. Certainly, one of the hottest culinary spaces within the New York fabric. What is your role there?”Across events-wine tastings, drag brunches, Restaurant Week, and holiday dinners such as Thanksgiving and Christmas Eve. Each one has its own personality and rhythm, and I am constantly challenged to adapt.”

It’s time for us to talk about wine tastings. That’s quite an upscale indulgence. What actually goes down behind the scenes?

“I’m working closely with sommeliers to know more about the wine profile. Set dishes and amuse-bouche match and contrast in just the right ways, and everything is dived deeply into the balance-acidity, sweetness, salt, umami. It is about the food that elevates the wine, not competes against it.”

Now that sounds like a high-wire act. You must be talking about something a bit more flamboyant : Drag Brunch.

“Yes! Drag Brunch is a party. It’s bold, energetic, theatrical. The food needs to reflect that energy. I think of dishes that are fun, vibrant, and still refined; think bright sauces, creative plating, and unexpected textures. It’s about channeling that vibe into the food. It pushes me creatively.”

And there’s Restaurant Week where it is all about access and variety.

“Exactly. We get all types of guests, be there a resident, or new one. The challenge, therefore, is to create a menu that speaks to Jean-Georges’ philosophy while also being totally approachable. No blunder allowed – every plate is the first impression.”

What are the dynamics of the holidays dinners? They should be hard.

Really. Christmas Eve, Thanksgiving, New Year’s, they have huge expectations with them. Guests want comfort but want to be wowed, too. A lot of time is spent on refining sauces, making sure the meat is cooked to the exact doneness. No broken emulsion. Soggy garnish? Not allowed.

line-up and pressing discussions regarding representation within our industry. The energy glows with chefs Carla Hall, Rodney Scott, Bryan Furman, Gregory Gourdet, Mashama Bailey, and Tavel Bristol-Joseph. It is just another experience being in the same kitchen with these legends.”

“Very much so. Christmas Eve, Thanksgiving, New Year-s come with expectations. The clients want comforts, but they also want to wow. The whole nine yards-that is creating a sauce and ensuring the beef is cooked to exact doneness. You cannot mess up emulsion. Soggy garnishes? Unforgivable.”

What were you working on during the event itself?

“There’s tons of prep work involved, and the kitchen turns into a cyclone with every detail counted. We do things like washing and trimming proteins-each one is then marinated and carefully laid on trays before going into the smokers. I worked on cutting and preparing vegetables for roasting and purées, with 100% adherence to all cuts and specifications. Chef Onwuachi designed this special curated menu to tell stories with each dish.

And you got to use some advanced techniques in there, right?

“I did. One of the highlights was working on tomato consommé spheres. It was modernist and twisted an old idea of traditional starch – using gastronomy techniques to encapsulate the essence of tomato in a clean, refined form. It’s these little things that can elevate an experience, and I was thrilled to be able to contribute something so refined to a menu so deeply rooted in heritage.”

That sounds like a real proving ground.

“It was. But what stood out to me just as much as the technical challenge was the camaraderie. Everyone’s learning from each other, lifting each other up. Best sense, it felt like family. And yes, Sheila Johnson-the founder of the resort-was there. I met her and took a picture. She’s got the same warmth and brilliance that define the event. That’s something I’ll take with me forever.”

What are some of your ideas on cooking tasks and cookery?

“When I’m in the middle of service, it’s got to be fast, yes, but it’s the grace that has always set me apart. Imagine a ballet dancer or a dancer doing the salsa. The rhythm of each one. The sauces are fragile. The proteins demand attention. And everything must be plated before that heart fades from the dish. ”

Your job looks so effortless.

“It’s not. But I have learned excellently how to actually show it. That is something that my days with Jean-Georges have shown me: discipline, consistency, and beauty in precision.”

In every wine pairing, every extravagant Sunday brunch, every sphere of consommé, Kalagara Thrushna is a guide as to the future of fine dining being built only by the memory, technique, and imagination of the expert. Amid minutes of execution and precision, she does not just contribute to the experience of fine dining in the United States—she is vital.

First Published: 28th October, 2024

Q 1: What inspired your journey into technology leadership, particularly in digital transformation?

A: It isn’t only about installing new technologies-it’s about replanting the business models, engaging customers more closely, and finding new avenues for generating revenue for me. What excites me most is pioneering change to create more effective operations, greater revenue, and better satisfaction for the consumer through innovation. This is one thing seeing the actual results of the transformation instills in me as a motivation to harness technology to shape the business of the future.

Q 2: How have you approached managing large teams and establishing an innovation culture?
A: Leading large teams begins with unambiguous and focused purpose-every individual needs to see how his/her work becomes a level higher in big transformative outcomes. And that’s where I am looking at the three pillars of empowerment and alignment tied with psychological safety. Empowerment means give over power and promote intelligent risks; alignment starts with transparent goals (like OKRs) and work across functions; and psychological safety becomes a must-for that is the cradle of innovation.

A systemic influence of creativity is rendered through the ‘fail-forward’ paradigm, acknowledging experiments as a means of learning. The hackathons, the development sprints, the open feedback loops are all critical tactics moving ideas towards actions-such that a daisy chain effect is created to ensure that sustainability is enmeshes with the that-negatable value of one’s urgent settling of innovation-massive impact!

Set in such a culture is the concept of recognition—which is the motor of the might! This recognition in the achievements choir, celebrating small as well as great successes: publicly and with frequency-so that real reinforcement is given to the behavioral direction that contributes to success. It is in a team meeting when a creative idea is mentioned to fix a problem or in a company-wide announcement where stars are bursting forth in the press, when hearts are rekindled with joy in everyone for the very mention of having worked on a project.

In its very end, it therefore proposes, essentially, to substance some environments that regard certain people as more significant, both encouraged and inspired. When teams feel safe knowing that their creatively are rewarded and their risks are prodded at, they are not the only ones accommodating changes-they in turn, start leading the change.

Q 3: What’s That Toughest Digital Transformation Initiative Which You Took and How You Overcame Its Obstacles?
A: One of the hardest things I had to deal with was doing the modernization of old inventory management systems into something more advanced-there being a complete migration to the cloud-native architecture. This project would eventually phase out the mainframe-based system that had existed for decades, replacing it with a more modern AI-driven solution with capabilities to optimize inventory in real-time.

Huge trials were the balancing of business continuity and large-scale data migration while getting multi-team stakeholder buy-in. Phased migration would gel this migration and allow the parallel operation of both old and new systems to minimize disruptions in workflow. I also introduced AI/machine learning-based forecasting models that subsequently improved demand prediction and inventory optimization significantly.

I believe very much in working with others’ contribution through which cross-functionality was achieved. To achieve the business goals applied to the technical execution, they had to work with engineers, data scientists, and business gurus. Leading to reductions in manual effort by 65% while gaining better accuracy in inventory with more agility to meet market shifts, especially those brought by disruptions like COVID-19.

Did this initiative strengthen much of my belief in a digital transformation that could drive operational efficiencies as well as longer-term resilience in business?

Q 4: How do you approach data governance and compliance in your technology initiatives?

A: Data governance and compliance are enablers of sustainable innovation instead of disablers. In my view, Governance needs to be embedded right from the architectural foundations of any technology initiative rather than an afterthought. Leading data-as-a-service platforms has taught me that effective governance has to balance accessibility and security.

For me, first of all, it involves coming up with a clear understanding of data classification norms and ownership models. Where automating compliance monitoring is feasible, it can be achieved easily with reduced maintenance efforts once the standards are in place. Cross-functional collaboration is one thing I have to organize regularly: I bring together technology teams, legal, and business stakeholders to ensure that our governance framework largely strides both regulatory and organizational needs.

I’m also a strong advocate for education—teams need to understand the “why” behind compliance requirements. We conduct regular training and create practical guidelines that help engineers implement best practices in their daily work. This educational approach has proven more effective than simply enforcing rules.

By approaching governance strategically, we’ve been able to improve data quality, enhance analytical capabilities, and build trust with stakeholders while maintaining strict regulatory compliance in all our initiatives.

Q 5: What role do you foresee AI playing in the future enterprise technology, and how are you preparing organizations for this new shift?
A: AI fundamentally reshapes enterprise technology-from being specialized functionality to becoming a necessity within virtually all systems that we build: human augmenting to predictive decision making; full-blown automated, end-to-end complex process automation; and creating whole new classes of personalized customer experiences.

Thus, to prepare organizations for this, we have focused on creating a multi-layer foundation, for example, we’re building infrastructure capabilities, like scalable data platforms that will enable AI systems to have feeds of really quality-governed data, high-reputation talent that acquires structured learning scalably through real AI-based projects that do this upskilling: both team and employee-focused initiatives. I am particularly keen on demystifying AI for business stakeholders-helping them see its real-world business relevant applications, and limitations, as well.

My approach is to endorse responsible AI adoption. I created frameworks for very much governing aspects such as ethics, bias detection, and transparency in AI. As evidenced by my work on the AI-based mental health diagnostic device patent, I believe the most valuable AI applications combine technological innovation with human-centered design.

AI would change everything as far as the enterprise is concerned- not anymore, as AI was formerly considered a specialized capability but now an integral part of practically every system we build. The top three areas: augment humans in decision making with predictive insights, or automate the entire process from end to end-smart integration to create entirely new classes of personalized customer experiences.

To prepare organizations for this shift, I build multi-layered foundation. For example, at the infrastructure level, we’re building scalable data platforms that would enable AI systems to be fed with high-quality, well-governed data. Upskilling teams through structured learning paths and hands-on AI projects is also applicable at the talent level. I’m particularly passionate about demystifying AI for business stakeholders—helping them understand its practical applications and limitations.

So this approach is also part of the responsible adoption of AI: I have created frameworks for ethical aspects as well as bias detection and transparency in AI systems. As demonstrated by patenting my work on the AI-based mental health diagnostic device, most valuable AI applications are the ones that incorporate technological innovations and human-based designs.

For those organizations that know AI as a simple technical implementation, the potential of AI will in no way be realized. Leaders of the movement will be those who could remodel their business around the capabilities of AI, under the echelons of strong ethicality and human centricity.

Q 6: How will you balance innovation with operational stability when introducing new technologies within the firm?
A: For me, this creates an opportunity for strategic alignment of change with agility while ensuring continuity of operations. Innovation is what gives competitiveness, but all of this must be done in a non-intrusive approach to core operations. It is through a phased, data-driven approach— pilots, testing, incremental rollouts, and scaling—that I am able to achieve this.

I was a part of some large-scale digital transformation initiatives like Marketplace and Inventory Management innovations. Here, I made sure that any innovation by AI/ML, automation, or cloud-natives did not have to judder the existing operations. The important perspective is KTLO-Keep The Lights On-maintaining and optimizing old systems and introducing new technology. It happens with solid change management, building redundancies, and constant monitoring of KPIs to assess in real-time the impact.

I promote a culture of innovation, but one that works within controlled parameters. The teams are encouraged to experiment, but under supervised conditions within a governance framework that prioritizes security, compliance, and system reliability. When aligned with business objectives, my risk management strategy ensures that innovation becomes a critical parameter for long-term success through operational resilience.

Q7: What advice would you give to the emerging tech leaders with large-scale digital transformations?

A: For the new tech leaders looking at digital transformation on a big scale, I would give some of the following nuggets of wisdom from my experience. First off, always remember that transformation is essentially about the people and organizing themselves to change around them-not simply implementing new technology. So start by building a vision that is compelling, encapsulating the `why’ behind the transformation, linking the technical changes to business recovery.

Stakeholder alignment is your only focus; determine who the power players are in the organization and invest heavily in getting them aligned. Build your army of supporters who will stand up for the transformation when the tide turns. Do not hide the good or the bad; people respect honesty more than they respect perfection.

When it comes to delivery, think big, but start small. Chunk the transformation process into bite-size pieces that can deliver value with minimum effort and quickly build momentum. Link these `quick wins’ to your overarching vision to build credibility with the whole project.

Finally, take care of yourselves and your teams. Transformations are marathons, not sprints. Build sustainable work rhythms, guard time for reflection and learning, and actively manage burnout risks. Your effectiveness as a leader will mirror that of your team in terms of health and engagement.

Keep in mind that an effective transformation is achieved not solely through technical brilliance but also through organizational wisdom. The most sophisticated technical solution will always fail if an organization does not put careful attention to change management, communicate transparently and understands with sincerity, those experiencing the transformation.

Q 8: Mentoring and developing the next generation of engineering talent- what do you have to say?
A: My own personal passion which lies at the very core of my responsibilities as a technology leader is in mentoring the next generation of engineers. My approach to mentoring focused on structured programs and spontaneous learning in the whole life of the program.
There is a powerful stretch assignment that forces engineers just beyond their comfortable limit and knowledge within the safety net of senior guidance.

I set ground paths for junior engineers with technical and leadership milestones. There are technical deep-dive sessions where we study problems together, thereby showing my thought process instead of merely dispensing solutions.

One impactful practice I’ve instituted is reverse mentoring, where junior members are given the opportunity to educate senior leaders on emerging technologies or approaches. This allows for reciprocity in learning while cementing the understanding that everyone has something valuable to teach.

I work with engineers to develop them as holistic beings: work not only for technical skills but also hone business sense, communication abilities, and ethical judgments. I believe there are “soft skills” that ultimately differentiate who goes into leadership, so I provide opportunities for engineers to present to business stakeholders, make calls during strategic planning, and participate in any way to groom their presence.

Mentoring is rewarding because the effects are multiplicative. Engineers I have mentored end up mentoring others, leading to a continuum of knowledge sharing and professional growth that essentially raises the whole organization.

Q. 9: What are your strategies to keep abreast of trends in technology-that evolve daily-and evaluate which ones to adopt?
A: To keep ahead in this fast-paced world of evolving technology, I believe that a very considered and multi-faceted approach is required. On the one hand and as an IEEE Senior Member, I follow innovative research and industry standards, engage in discussions with peers, and keep myself informed about what is out there as a means to check emerging trends. On the other hand, I keep a diverse network of technology leaders from many industries, since the cross-pollination of ideas often germinates the most innovative and practical solutions.

A framing method is employed through which I juxtapose the technical merits versus business alignment in order to assess the adoption of trends. Business alignment is given priority to technical solutions that alleviate real pain points, enhance efficiencies, or create new opportunities in line with strategic objectives. Getting swept away in the hype cycles is easy; however, I have mostly focused on identifying the underlying capabilities that will remain in demand past short-term trends.

I’m also a strong advocate of hands-on experimentation. We conduct regular technology radar sessions to track promising innovations, followed by targeted proof-of-concept projects to validate their potential in our specific context. This pragmatic approach ensures that our technology investments are driven by real-world applicability rather than theoretical appeal.

Equally important is knowing what not to pursue—no organization can chase every promising technology. I emphasize a focused strategy, identifying the few trends that could be truly transformative and concentrating efforts there, rather than diluting resources across every new development. By combining strategic foresight, practical testing, and disciplined prioritization, I ensure that technology adoption drives tangible business value rather than innovation for its own sake.

Q 10: Looking ahead, what are your long-term aspirations in technology leadership, and how do you envision the future of digital innovation?
A: Looking ahead, my long-term aspiration is to lead transformative initiatives that blend technological innovation with positive societal impact. My work on the AI-based mental health diagnostic device patent reflects this direction—using advanced technology to address meaningful human challenges. I aim to move beyond traditional enterprise technology leadership to shape how organizations leverage technology for both business results and broader positive change.

I envision the future of digital innovation becoming increasingly interdisciplinary. The most valuable breakthroughs will emerge at the intersection of different domains—where AI meets healthcare, where sustainable technology meets consumer products, where immersive technologies transform education. I’m particularly interested in how technology can enhance human capabilities rather than simply automating existing processes.

I also believe we’re entering an era where responsible innovation becomes a competitive advantage, not just a compliance requirement. Organizations that build ethical considerations, sustainability, and inclusivity into their technology strategies will outperform those focused solely on traditional metrics. I aspire to be at the forefront of defining these new models of responsible technology leadership.
Ultimately, my goal is to build and lead organizations where technological excellence and human-centered values reinforce each other—where we measure success not just by what we build, but by how it improves lives, creates opportunities, and solves meaningful problems. That’s the legacy I hope to create in my technology leadership journey.

About Siva Kannan Ganesan

Siva Kannan Ganesan is a Principal Engineer and distinguished technology leader with over 24 years of experience in digital transformation. Holding a Bachelor of Engineering in Mechanical from Vellore Institute of Technology (VIT), India, Siva has made significant contributions in multiple leadership roles across global organizations. His areas of expertise include enterprise architecture, cloud modernization, and AI-driven innovation.

Siva is also an inventor with a UK Registered Design Patent for an ‘Artificial Intelligence-Based Mental Health Diagnostic Device’ and an IEEE Senior Member. Throughout his career, he has led large-scale technology initiatives, managed multi-million-dollar budgets, and built high-performing teams. His strategic leadership has positioned him as a thought leader in digital transformation, with a reputation for driving innovation and fostering organizational growth through advanced technologies.
In addition to his technical achievements, Siva is a recognized industry speaker, sharing his insights on digital transformation, AI, and cloud innovation with technology leaders and organizations worldwide.

Q 1: What got you interested in telecommunications, and particularly carrier certification and testing?

A: My interest in telecommunications was sparked by just how rapidly mobile technology interfered with the day-to-day activities of human beings. Carrier certification turned out to be particularly interesting to me because it represents a kind of crossroads between innovation and reliability. Working in this area makes it possible for me to directly see cutting-edge technologies–5G SA & NSA, LTE, and other cellular wireless technologies–ensuring that they meet rigorous specifications prior to being made available to consumers. The thrill is in being part of that process which brings new capabilities to communications, knowing that my work is helping to advance technology in millions of user’s lives.

Q 2: What is your approach to managing cross-functional teams for complex technological projects? 

A: To me, handling multidepartmental teams would involve strong and clear communication and that each member of the team has an appreciation of each other’s individuality. The specific objectives of the project becoming considerable are shared with everyone, bearing in mind that every person having a well-defined understanding of their individual contributions towards it. The regular visit checks and visible project tracking will act as a momentum keeper and flag for any rising issues. I’ve noticed that an environment where team members feel free to talk about what difficulties they experience leads to even more exciting solutions. I also push creating of relationships outside departments so that you are not siloed in, especially for those tricky carrier certification jobs, where they’ve got to cross over engineers, quality assurance teams, and business stakeholders.

Q 3: How have you managed to see the growth of the 5G technology and what challenges did you face from the certification perspective?

A: Well, I’ve seen the journey of 5G from inception to massive deployment, and I would say from the experience-it has made life very different from 4G, bringing tremendous improvements in these areas: speed, latency, and connection density. However, these achievements have brought with them certification challenges. For example, with the deployment of millimeter wave technology (FR2), testing becomes more complicated due to signal propagation characteristics and the development of specific measurement methodologies. One of the most important challenges among them has been to keep the performance consistent over different environments, mainly for FR2 deployments that will be more sensitive to obstructions. Furthermore, there are many cases that need to be tested to guarantee the interoperability between 5G with 4G and legacy networks. To combat these challenges, I have implemented stricter field testing protocols and spent adequate time training engineers on the nuances of 5G technologies to ensure our certification procedures remain thorough and reliable despite the increased complexity.

Q 4: What are the methods you apply to keep the working project within the time and budget limits?

A: Timely and budget-wise completion of the telecommunications project required a combination of detailed planning and flexible execution. Thus, these projects start with extensive project scoping so that the deliverables can be broken into manageable milestones that have a buffer time for unexpected events. It includes resource allocation- I do assess the team capacity and expertise so that I can put right people on the correct tasks. For budgeting, I do regular financial reviews and compare actual expenses and projections to identify overruns at an early stage. For carrier certification, the best way I have found to use resources wisely in the most stressful times is to test priorities up front. In addition, I make use of project management tools, such as JIRA, to keep everything visible throughout the workstreams to enable data-driven decisions whenever adjustments need to be made. But most importantly, I create an accountability atmosphere in which people are encouraged to take ownership of their work but feel supported when they face obstacles.

Q5: Here were the exciting advancements in telecommunications technologies for the future. 

A: I think the area where I am most excited for the future is convergence 5G technology with the other upcoming developments, such as artificial intelligence and IoT. Ultra-low latency and a large number of device connections create possibilities. Looking forward to being able to see how network slicing would change the way different applications allocate network resources, innovations involved in developing 6G technologies capable of operating in terahertz frequency ranges, and integrations of satellite-based communications with terrestrial networks.

These developments together open up exciting possibilities for global connectivity. Beyond the new technology, I personally think these developments are going to open up new possibilities with application fields such as smart cities, autonomous vehicles, and telemedicine since they will require different approaches to certification and testing. I am looking forward to working into those standards to ensure that those technologies can count on being reliable, secure, and accessible. 

Q 6. How do you keep abreast of the fast-moving and ever-changing technologies and standards within telecommunications? 

A: Current developments in telecommunications require a multidimensional approach, as innovations are awakened nearly every second. Direct communication with attendees and speakers was possible at a conference, seminars, and webinars by industry experts. I also get a front row seat to new technology development when I work with carrier certification before most of the rest of the marketplace has been exposed to it. I remain active in the professional bodies and keep reading the reputable journals like 3GPP and IEEE. Colleagues within an organization who are specialized in different branches also offer different perspectives, and according to their ingenuity, they could be resourceful in furthering practical knowledge from theory. The same goes with new technologies as hands-on learning experiences develop theoretical and practical knowledge in telecommunications. I’ve also been able to develop a network of contacts with industry colleagues for periodical exchanges of ideas and information. This unique combination of formal education, hands-on applications and professional networking has been critical to maintaining a current skillset in the last 15 years of my telecommunications career. 

Q 7: What piece of advice would you give to someone wanting to get into telecommunications, particularly in testing and certification? 

A: For people entering the telecommunications testing and certification segment, I would suggest first learning networking basics and wireless technologies really well. Whether they specialize in a certain area, having understanding of protocols, signaling or RF principles would benefit them later on. Get hands-on experience, even if entry-level, because such know-how is invaluable in the field.

 Certifications relevant to telecommunication would definitely help in proving the commitment to the authenticity and expertise with which a person can explain to future employers. Be prepared to learn on and on because this industry transforms very quickly – what was cutting edge today will be standard tomorrow. Develop analytical and problem-solving skills because troubleshooting is one essential aspect of such testing work. Do not underplay communication skills, for instance, with technical ability one needs to relay that information to different stakeholders, ranging from engineers to business leaders. Tremendous room for growth is offered in telecommunications, especially now with 5G coming into the mainstream within companies, as well as research advancing in 6G; hence this is a great time to join the industry. 

Q 8: High pressure situations, especially around certification deadlines, need to be handled with balanced strategic planning and tactical execution. For example, now I’m facing tight timelines; I reassess priorities and focus efforts on critical path items affected by certification. Clarity on the communication plan becomes much more important-I also ensure that relevant stakeholders know what our plan of action is and what risks to expect. I’ve found that breaking large problems into several smaller ones is a great way to boost morale within the team and create solid indicators of progress. During extraordinarily intense certification cycles, I implement check-ins as much as possible but also focused so that blockers can be cleared at an accelerated pace without infringing the flow of work. I have developed throughout my entire career a problem-in-analyzed fashion, which helps much faster resolve technical issues that would derail the time for the completion of the certification. Most importantly, I keep a calm, solution-oriented approach so that the team can keep their focus and not feel the pressure. This has really helped us meet challenging deadlines while maintaining testing quality and team wellness.

Q 9: What are your long-term career aspirations in the telecommunications industry? 

A: On the long run, I look forward to holding a strategic leadership position whereby I can influence telecommunication technology adoption and implementation. I am particularly passionate about helping organizations realize the potential of emerging technologies such as advanced applications of 5G, private networks, and eventually 6G. As someone who has spent a lot of time in carrier certifications, I see extraordinary value in bridging the gap from technical innovation to practical deployment. I want to leverage the experience to energize great initiatives that hasten the responsible deployment of new telecommunications capabilities while ensuring those capabilities meet the most rigorous standards for performance and reliability. Also, I’m deeply involved in mentoring the generation-younger telecommunications professionals so that these insights get shared throughout my career. I would like, in the end, to contribute toward how such transformational technology better connects and opens new opportunities for different industries and communities.

About Kranthi Kiran Kusuma 

Telecommunications professional with 15 years of experience in the industry, specializing in carrier certification and project management. Kranthi Kiran Kusuma works based in Ontario, California, having a proven record in managing complex technology projects and cross-functional teams. Kranthi has expertise in 5G( FR2/FR1 ), 4G LTE, as well as other wireless technologies. He has successfully led multiple field certification programs across North America. Including a Master of Science in Information System Management, a Master of Information Technology, and a Bachelor of Computer Applications, Kranthi’s educational qualifications are great. Throughout his career, Kranthi has had strong linkages with industry key clients while ensuring product delivery success and technical excellence in the telecommunications industry.

Q1: To what inspired you to choose this career in data integration and business intelligence?

My academics in Electrical Engineering have much to do about fiber optics, which piqued my curiosity about how data moves and, more importantly, processes it. As a result of this curiosity, it became natural for me to want to look into ETL, data warehousing, and BI. I really enjoy being behind the scenes transforming raw data to information that can drive decision-making within the business. I enjoy solving complex problems and designing scalable solutions, and I love learning continuously in this fast-changing field.

Q2: How do you approach the technical requirements elicitation process and what are the critical points to be observed?

I emerge from a collaborative, vigorous process of exploring one’s needs and challenges with stakeholders, analyzes current data systems-high complexity and quality, aligns technical dimensions within their business objectives, scoping scalability, performance and security, and nothing gets built without documentation and validation with stakeholders. Here, I concentrate much on solution building, being both technically feasible and delivering bottom-line value to your business.

Q3: Please give an example of a difficult project you handled and how you managed through the barriers:

I led a high-pressure insurance claims integration project that involved legacy mainframes and poor data quality and required early delivery. To handle this,

• It’s implemented by phased rollout to compartmentalize the mazes.
• Automated processes in used under Autosys and Control-M.
• Setup daily stand-ups for issue resolution and strict data validation. This created a high-fidelity deliverable under tight timelines.

Q4: What is the role of automation in your data integration approach?

Automation forms the base of the work that I do. It improves the productivity and consistency of operations, scales with the volume of digital data, and frees up resources for strategic tasks. I schedule and monitor tasks and jobs using Autosys and Control-M, leaving automated tools to take on the big, complicated integrations, especially with costs on legacy systems.

Q5: Best Practices Inclusion in ETL Development Work

• Modular reusable ETL components.
• While dealing with complete documentation, one must also bring into focus data lineage.
• Error trapping and very strong logging and validation.
• Query optimization and peer review.
• Version control and uniform coding standard.

This would make the solution high-quality, maintainable, and scalable.

Q6: What are the most common tools or technologies you use, and how do you keep up with new trends?

I have a wide toolset that includes Informatica ETL, SQL databases like Oracle and MySQL, automation through Python and shell scripts, cloud platforms (AWS, GCP), and job schedulers such as Autosys and Control-M.

To keep abreast of the latest happenings, I invest in various online courses, track industry-specific blogs, interact on GitHub and Stack Overflow, collaborate with peers, run experiments through proof of concepts, and attend conferences/workshops. It is through constant learning and hands-on experimentation that one stays updated.

Q7: What strategies do you use to manage cross-team collaboration during complicated data integration projects?

I emphasize the importance of clear communication, assign roles using the RACI matrices, and followed Agile methodologies (Scrum, Kanban) through tools like Jira.

Building relationships-organizing workshops, dragging those technical concepts into the language of business, making visible documentation and dashboards-regular feedback sessions to keep all stakeholders aligned and agile.

Q8: What would you recommend to a person trying to walk into the field of data integration?

Get his SQL and databases down well, learn every ETL process that he can get practical at, learn his Python or shell scripting for automation, get to know cloud platforms (AWS, GCP, Azure), develop some acumen for business, and look to improve communication.

So, curiosity and continuous learning, quality of data, and networking with professionals would go far. A mix between technical strength and business understanding is a key factor in success.

Q9: Generally, how would you deal with data quality challenges in integration projects?

There are clear standards for early data profiling and data quality metrics. At the same time, I tend to apply validation rules and automated monitoring.

Resolving any identified issues is closely done with the data stewards while documenting the resolutions made. Trying to do it right at the start builds trust and cuts down the later effort of having to redo.

Q10: If you cast your gaze far into the future, where do you see yourself in the company? What steps are you taking or planning to take in order to achieve this?

My vision for the future is to drive and deliver data strategies for enterprises through cloud competency enhancement, AI/ML training, and data governance knowledge enhancement.

I am concentrating on business skills, communication, and leadership abilities in order to bridge the gap between technology and business needs.

I am for automation-build error-free and failure-tolerant workflows that increase speed and reliability to allow teams to focus on strategy work.

About Kishore Ande

Kishore Ande is a data integration specialist with a penchant for designing efficient automated data solution processes with sound educational background inputs through electrical engineering. With expertise in ETL development, business intelligence, and data warehousing; Kishore possesses a remarkable ability to put together and manage technical integration projects in a very agile manner across the retail and insurance industries. His stack-wise technical expertise includes Informatica, all varieties of SQL Technologies, cloud platforms-Amazon Web Services, Google Cloud Platform, real-time processing, AI/ML, and automation tools. During his professional life, he has carried out the entire life cycle of management of complex data integration projects with special emphasis on product data management, pricing systems, and vendor management specifically in retail and insurance industries. He balances technical skills and leadership qualities to give varied value proposition among different business environments.

Q1: What made you want to become a pioneer in machine learning and engineering leadership?

A: It all started during my study when I had a firm grounding in mathematics and computer science and was enthralled by the reality of how machine learning could solve complex challenges coming from the outside world. What truly draws me to this field is the unique combination between the technical challenge and tangible impact. With regard to leadership, I found that I actually enjoy mentoring engineers while marrying technical excellence with business outcomes. This dual passion shaped my career path from hands-on technical roles into engineering leadership positions where I can influence both technology direction and team growth. 

Q2: You worked on ETA and routing optimization. Could you tell us about challenges in that area and your approach to handling it? 

A: ETA and routing optimization is exciting from a machine learning, real-time data harnessing, and logistics standpoint. The primary challenge it poses is balancing speed and accuracy-the predictions should be accurate and instantaneous. 

So my approach was around iteration for optimization. We built the ML models, infused with real-time feature support through technologies such as Kafka and Flink, more increased correctness and conversion rates. I was the lead on multiple iteratives of ETA models, each addressing different edge cases and where those bottlenecks in accuracy lay. What made this so satisfying was that one could see how relatively small increases in prediction accuracy could translate into astounding business impact.

Q 3: How do you build and lead high-performing engineering teams?

A: In fact, the formation of high-performing engineering teams shall begin during recruitment, finding the right people not only with the qualification but also having a view of growing their mindset and willingness to collaborate. As for the assembled team, I would concentrate on three areas: clarity, growth, and culture.

Clarity would be to make sure everyone understands our tech vision and how their piece fits in the grand scheme of business objectives. I usually build multi-year technical visions and break these down into executables with clear success metrics.

As for growth, I invest heavily in mentoring and provide such stretch opportunities for engineers. Across my career, I’ve mentored innumerous engineers to grow technically and develop parallel action capabilities. 

I would say that the most important is culture – a culture in which they can communicate openly, where it is safe to experiment and fail, and where we celebrate our victories together. I design processes for healthy on-call rotations, good planning cycles, and cross-functional collaboration. The proofs are in the pudding because my teams always rank top for engagement, and they produce great business outcomes. 

Q 4: What role does fraud prevention play in technology platforms, and how have you approached this challenge?

A: Fraud prevention, the least glamorous of components of a technology platform, is also the most critical. It dictates not only financial performance but also trust by users. Patterns of fraud are evolving continuously making the challenge more complex as it requires systems that can quickly give pay-back time when anticipation fails.

My approach has been to develop systems that are proactive and adaptive rather than reactive. This means building, training, and deploying machine learning models that would be capable of detecting suspicious behavior before it turnsinto major losses in regards of not having robust verification procedures in place as well as multilayered approaches of defense models. I’ve led teams focused on various fraud vectors – from promotion abuse to account takeovers.

What FRAUD defense has in its point of interest is this frictional creation: the balance of a solid safety net for the user experience. The strictest safety nets tend to make real users unhappy, while the worse ones make it easy for dishonest users to steal. Finding that perfect center point requires joint efforts between engineering, product, and operations, and I have actually put much emphasis on that throughout my career.

Q 5: You have worked for the cause of energy disaggregation. Could you explain the concept of energy disaggregation and its significance pithily?

A: Desegregation of energy is a thrilling technology that breaks down energy usage for households or businesses into individual appliances and devices–simply put, it tells you how much energy your refrigerator, HVAC system, or other appliances are consuming without the need for installing individual meters on each device.

Justifying the importance of this technology could lead us to a multi-faceted approach. For consumers, it offers practical insights to identify energy-guzzling appliances or poor usage patterns so as to reduce energy costs. For utilities, it introduces personalized energy-saving recommendations and improved demand prediction. Lastly, and from the perspective of sustainability, it has been shown that mere visibility into your energy consumption can help in reducing it by a range of 3 12%–a massive sphere of environmental benefit in its own right.

In the field of energy disaggregation, my stretch in the energy business witnessed the filing of many patents on innovative affordable ways of disaggregating energy. These included humility to work with tight datasets. We came up with hybrid frameworks that used machine learning model approaches with signal processing systems to achieve intelligent detection, even when supported by sparse meter readings per month. In practice, the technology has influenced more than millions of users worldwide, an example in terms of how technical innovation can be used to achieve both business success and a wider environmental impact.

Q 6: What technologies and tools do you find most valuable in your work, and why?

A: My set of tools has evolved over time, but there are a few technologies that I have always found valuable. Java and Kotlin, as my go-to languages of choice, have always been very good to build robust distributed systems for their performance and reliability. Python remains a favorite for having its vast ecosystem pertaining to data science and machine learning.

On the infrastructure side, stream processing frameworks such as Kafka and Flink have been transformative in the building of real-time systems with high throughput. We use AWS services such as Aurora (PostgreSQL), EC2, and S3 to build scalable solutions with the least complexity of infrastructure. Every so often, databases such as CockroachDB and Cassandra are also used to manage large operations quite effectively.

Jupyter Notebooks and Databricks, with their flexible environments for quick hypothesis testing, provide perfect prototypes for data analysis and experimentation. And for offsite monitoring and system health, I have found truly excellent telemetry systems – absolutely critical.

What informs my choices in technology is not the latest fad, but rather relevance to the specific problem at hand. Sometimes, a simple SQL query might outperform a complex machine-learning model, and when to use which comes only from experience and continuous learning.

Q 7: How do you approach the intersection of academic research and practice in industry?

A: That’s some of the most incredible work getting done today, in my opinion – at the interface of what is being discovered in purely academic research and finding use within application in practice. I’ve been able to straddle the two worlds over the course of my career: publishing academic articles that have attracted their nice collection of citations while also taking legacy that is manifest onto bottom-line business.

My approach to this intersection follows three principles: The first is that one ought to be up-to-date on research but pretty selective – not all research breakthroughs spill over into practical benefit. Second, change instead of participate; most of the time research doesn’t hold unless modified enough to function with “real” constraints of the production environment. The last is give back – to me, this would be more like publishing our learnings every once in a while, such as my papers on deterministic annealing for clustering and vehicle routing.

Specific to this would be my routing optimization work, which contained a lot of academic research behind problems like time windowed vehicle routing but incorporated fairly practical constraints like real-time traffic feeds and driver preferences. The result has been a solidly sound theoretical foundation and practical effectiveness. 

Q 8: What advice would you give to someone looking to pursue a career in engineering leadership?

A: The first thing I would highlight for aspiring engineering leaders is building a strong technical foundation since great leaders in our field understand the technology enough to make wise architecture decisions and can earn the respect of their teams. 

It gives deliberate opportunities for influence without authority. Lead a project, mentor junior engineers, or work on cross-functional initiatives. These experiences give you your leadership muscles in lower-stakes environments.

Q 9: How do you stay current with industry trends and emerging technologies?

A: Staying current in our rapidly evolving field requires intentional effort. I follow a multi-faceted approach that combines formal and informal learning opportunities.

For formal learning, I regularly take courses and workshops on emerging technologies. I also attend key conferences where cutting-edge research and industry applications are presented. These structured learning environments help me build foundational knowledge in new areas.

For day-to-day learning, I follow influential technologists and researchers on platforms like Twitter and LinkedIn. I subscribe to newsletters and blogs that curate important developments in machine learning and distributed systems. I also set aside time each week to read academic papers that might be relevant to challenges we’re facing.

Perhaps most valuable is my network of peers across different companies. We regularly exchange insights on technologies we’re exploring and lessons we’re learning. This collaborative learning approach provides perspectives I wouldn’t gain on my own.

Finally, I believe in learning by doing. When a new technology shows promise, I’ll often build a small prototype to understand its strengths and limitations firsthand. This practical approach helps separate genuine innovations from hype.

Q 10: Future of machine learning in business applications, what are your career goals in this transforming landscape?

A: The future of machine learning in business applications is towards more integrated and contextual autonomous systems. From having isolated ml models, we will move toward ai systems, which will understand the context and make decisions toward an appropriate level of human supervision and will learn continuously with new data.

Three trends particularly turn me on: First, democratization of tools of ml that allows domain experts to use ai without deep technical expertise. Second, building of more transparent and interpretable ai systems for better human-ai collaboration by building user trust. The third that excites me is the application of ml in domains not touched so far for tremendous value even with small improvements.

Aspirations of mine would lead me to places in which I could be responsible for the strategic management of both technology and organizational culture, and eventually, environment in which talented engineers and data scientists would feel inspired to do their utmost work on problems that matter. Heavily depends on continuous learning, which will be even more important in the future, as the field reshapes. 

About Pratik Mayur Parekh 

Pratik Mayur Parekh is an engineering leader with an edge in machine learning, distributed systems, and fraud detection. With the power of advanced degrees from the University of Illinois at Urbana-Champaign and IIT Kanpur, Pratik has spearheaded multiple engineering teams, carving out significant business impact from technology innovation. These patents include multiple patents in energy disaggregation, impressive academic publications with citations above 30, and MI systems processing tens of thousands of requests per second. Pratik has the capability of technical depth with leadership to create high-level cohesive engineering teams that can actually deliver results quite easily.

Q1: What attracted you to product management specifically for e-commerce and AI/ML industries? 

A: I have been passionate about product management arising from a strong desire to bring solutions that can target business benefits versus consumer satisfaction.

 The e-commerce industry excites me because it is a never-ending state of flux and one that constantly brings forth challenges at the intersection of technology, user experience, and business strategy. Concerning AI/ML, I have always liked the fact that AI/ML technologies can transform how we enhance customer experiences and optimize operational performance. By product managing across these domains, I put myself in an enviable position where technical innovation gets aligned with real-world applications and measurable business outcomes.” 

Q2: What is your approach to leading new go-to-market strategies for new products/features? 

A: My approach to go-to-market strategies is very collaborative and data-driven. My starting point is to ensure the products adequately meet customer needs and pain points, constantly in touch with the research teams to validate assumptions. Afterward, I engage cross-functional teams, including engineering, data science, legal, selling/account management/vendor management professionals, marketing, and customer support, to forge common ground on messaging, timing, and execution plans. For example, in leading the go-to-market of a generative AI product, I ensured stakeholders were completely on the same page in terms of technical capabilities and business impact, whereby resulting in a smooth launch that increased sales conversions. I believe that successful product launches require equal attention to product excellence, operational readiness, and strategic internal and external coms.

Q 3: How have you used machine learning to address business problems?

A: Machine learning for me is an amazing tool that can solve difficult business problems, which is impossible with the conventional ways. One of the major problems I took on was search relevancy, improving it by trying to measure and reduce catalog metadata incorrectness. This was successfully achieving measurement and minus error across 8 countries via a multi-modal machine learning approach of structured text, unstructured text, and images. In another instance, with respect to enhancing the fraud detection algorithm accuracy from 60% to 98%, led strategy to integrate cybersecurity IP detection and multi-factor authentication. The key is finding the right problem where ML can give you an exponential rather than incremental improvement, and then carefully engineering solutions to solve it while always keeping business needs in view. 

Q 4: What methods did you find effective in growing an early-stage product into a significant revenue generator?

A: Growing a product is all about balancing between vision and execution. I was actively engaged with Pitney Bowes between 2017 and 2021 when the API for Shipping emerged as a $100 million-plus ARR business over a four-year journey. The singularly most effective “strategy” was to keep the undiluted customer focus through the roadmap-building effort. We would keep talking with them, trying to interpret changing needs, prioritize the highest yield features, and seamless onboarding-the time for an API integration reduced from 5 days to just 10 minutes through SDK development. It would be that metrics were built to follow progress, and we were not shy from doing what was necessary to pivot. Products built to real problems, with constant iteration on user feedback, are the most sustainable ways to grow.

Q 5: At which stage customer opinions impact product development? 

A: Product development is built on customer feedback, which does hold some sway in the theories behind product development. I collect insights through various ways from direct customer interviews, usability tests, analytics data, and feedback through NPS surveys. My work history would include having conducted focus groups by the dozen and client surveys and listening to customers’ pain points and areas of opportunity. Another major point being that the value of this feedback is not just the collection of it, but what is done to implement feedback towards real product enhancement. User feedback gathered during the merchant and developer portal improvement iterations were implemented radically improving the Net Promoter Score in our SaaS API services by 500 basis points. I believe in creating loops with customers to show them how their feedback is directly influencing product development. This builds further customer loyalty and nurtures customer engagement.

Q 6: What tools and methodologies are relied upon for effective product management?

A: Essentially, I use various tools that can support new product success through the entire development cycle. Google Analytics, Adobe Omniture, Hotjar, and UserTesting.com can tell you about all sorts of user behaviors. For the planning and execution of products, we had Jira, Balsamiq, and Figma to prototype A/B testing frameworks while Googling Analytics. But for my tools, I lean more toward agile methodologies, intending to identify regular sprint planning, daily standups, and retrospectives to keep the impetus and learn along the way. Looking at product roadmaps, we are data-driven and will build out quantitative metrics against qualitative insights…This is the thinking that helps us to set the right feature in the right way so that we always have an outcome focus.'<br>’

All through the management of cross-functional teams, it becomes most helpful to create an enabling environment of shared purpose and structure for effective communications. I have often coordinated product development in several matrix organizations across a wide variety of functions: sales, marketing, law, engineering, ML operations, and customer support, covering many geographical landscapes. To me, precelebration of wins and establishing clear roles, responsibilities, and success metrics up front in the project is most important. I try my best to respect others’ time for regular touchpoints and alignment. Good documentation throughout is paramount so all decisions and rationale are recorded and readily available. Creating a culture of open conversation about hurts, help, and ideas-for-all members-excluding no rolodex or rank-was also emphasized. Recognizing diverse points of view and celebrating team successes builds high-performing cross-functional teams that are results-oriented and very consistent.

Q8: Give Advice to Someone Approaching Product Management:

A: Advice for product management aspirants is to start building a strong foundation in the hard and soft skills random. Look for opportunities where one can really understand what the customers go through either by internships, in customer-focusing work, or even in projects that you can do yourself. Most importantly, develop your analytical skills to extract data into meaningful insights into possible actions. Important as well are communication skills; practice making difficult ideas simple and convincing. Include perseverance; this field is quite competitive, so do not let rejection dampen your spirits. I started my journey with an internship at a real estate site – while there I did customer surveys and focus groups-and reported straight to the CEO. Those first lessons were ways of figuring out what users need, and translating them into business opportunities. And while you do this, keep questioning, and keep learning; product management changes very fast, mostly from the new technologies of AI/ML.

Q9: How do you keep up with the industry’s changes and new technologies? 

A: Keeping oneself abreast of the changes in the fast-evolving world of technology demands intentional and deliberate time and curiosity. This is precisely what would apply much in a year of attending industry conferences and webinars pertaining to product management, e-commerce, and AI/ML innovations.

Thought leaders in the space are followed on platforms like LinkedIn and Twitter, as well as newsletters and publications that focus on trend-emerging topics. Being at the heart of professional communities and networking events offers diverse perspectives on challenges that peers might be facing. Not to mention, creating time to personally dabble in technologies, say fine-tuning LLM with guardrails as well as RAG architecture, is my method of understanding their practical viability. Learning never ends in this field is the reason currently pursuing an Executive MBA at Wharton to get better on the business side with some technical knowledge. 

Q10: What are your long-term career aspirations? What steps are you currently taking toward those aspirations?

A: My ultimate goal is to provide success with product strategy and innovation at the executive level, an arena where I can set down the path for the organization and nurture other product leaders. What really excites me is working in a space where AI/ML meets consumer-facing products and has tremendous potential for creating transformational user experiences. Opportunities in this space are what I am working toward by doing an Executive MBA at Wharton School of Business for further development of my strategic-thinking and leadership competencies. Further, I constantly seek out opportunities for tough product work that increases my capability set for different domains and business models. Over my entire career, I have a proven ability to affect business through product innovations, ranging from conversion improvements on millions of products in 21+ countries to ML algorithm accuracy improvements from 70% to 95%. These opportunities combined with my ongoing learning and networking will launch me into leadership opportunities, where I can have a larger impact on organizations and industries.

About Yaswanth Jeganathan

Yaswanth Jeganathan is a product leader with nearly 10 years of proven technology industry experience, passionate about using AI/ML innovations to grow businesses. Yaswanth’s strong academic foundation from Wharton, Carnegie Mellon, and Anna University is complemented with proven experience in delivering end-to-end e-commerce products, SaaS products, and AI innovations. Top career achievements include growing a SaaS API product with over $100 million in Annual recurring revenue and leading go-to-market strategies for AI initiatives that lifted conversion rates by a major percentage. Yaswanth possesses the rare mix of technical depth and business insight required to create profound product experiences in diverse global markets.