This interview is with Rajesh Pandey, Principal Engineer at Amazon Web Services.

Rajesh Pandey, Principal Engineer, Amazon Web Services

Can you introduce yourself and share your current role in the tech industry?

I’m Rajesh Kumar Pandey, a Principal Engineer at Amazon Web Services (AWS), where I lead the design of large-scale distributed systems within the AWS Lambda organization. My work sits at the intersection of serverless infrastructure, event-driven computing, and the evolving needs of AI workloads. At its core, I help build the invisible plumbing that allows teams across the world to run mission-critical systems reliably, scalably, and without needing to think about servers. Over the past decade, I’ve worked on some of the foundational systems that power the modern cloud: building resilient async architectures, inventing new polling and scheduling mechanisms, and driving platform-wide efforts to make serverless systems more observability- and cost-aware. I hold over 10 patents in distributed systems and serverless infrastructure, and I frequently write and speak on the challenges of scaling real-world cloud-native systems, including recent work on the infrastructure patterns required to run Generative AI at scale.

Outside my day job, I share insights through publications on InfoQ, IEEE, and my Substack “Cold Starts,” and I actively mentor engineers and teams trying to turn complex systems into elegant solutions.

What inspired you to pursue a career in technology, and how did you navigate your way to your current position?

My journey into technology began with curiosity – pure and simple. I wasn’t the kid who dreamed of being a CEO or a startup founder. I was the kid who took apart toys to understand how the motor spun, who stayed up late tweaking code to make pixels behave just right. Technology fascinated me not just as a tool, but as a language – one that could translate human intent into real-world impact.

Early on, I gravitated toward systems, particularly those that had to work at scale. I was drawn to the invisible glue that holds software together: distributed coordination, failure handling, backpressure, latency trade-offs. The deeper I went, the more I realized that complexity wasn’t the enemy – unacknowledged complexity was. That insight shaped much of how I approached my career.

I joined Amazon Web Services at a time when serverless computing was still evolving. Over the years, I became deeply involved in designing and scaling AWS Lambda’s event-driven infrastructure. This meant grappling with real-world problems – retries that cause hidden amplification, cold starts that create unpredictable tail latencies, GenAI inference workflows that strain traditional assumptions about statelessness. I wasn’t just building infrastructure – I was helping define how modern workloads run at planetary scale.

Getting to this point has been less about climbing ladders and more about following threads. I’ve said “yes” to hard problems, built internal tooling that quietly made systems more reliable, and mentored others on how to think long-term about architecture. Along the way, I authored patents, wrote for platforms like InfoQ, and most recently had the chance to share my story in TechBullion. Each of these moments was driven by a core belief: that the best engineers don’t just ship features – they create clarity where others see chaos.

Now, my focus is on helping teams run GenAI workloads with the same reliability we expect from traditional systems. The future isn’t just about smarter models – it’s about infrastructure that can keep up with them.

You’ve mentioned the importance of adapting in engineering. Can you share a specific instance where your ability to adapt significantly impacted a project or your career?

One of the most defining moments in my career came during the development of a critical event ingestion system at AWS. The goal was to handle high-throughput workloads in a serverless, asynchronous architecture—something that looked clean on paper but quickly became chaotic in production.

We initially leaned into standard retry and failure-handling mechanisms. But once we went live, we noticed a disturbing trend: downstream services would intermittently collapse under pressure, even when everything appeared “green” on dashboards. Latency spikes and silent retries were masking real issues, and our observability tools weren’t catching them in time.

Rather than rigidly sticking to the original design, we stepped back and re-evaluated from first principles. I proposed a shift: instead of layering on more defensive code, we would build adaptive backpressure-aware retry orchestration, coupled with real-time event visibility. This meant reshaping parts of the architecture to not just “try again,” but to understand when and why to pause or reroute.

That change made all the difference. Not only did we stabilize the system under burst load, but we also created patterns that were later adopted more broadly across the platform. More importantly, it taught me that resilience isn’t about preparing for failure—it’s about designing systems that evolve as reality unfolds.

Adaptability, for me, isn’t a reaction to failure. It’s a mindset of staying curious, questioning assumptions, and recognizing that the best technical decisions often come after you’ve seen how the system behaves under real-world conditions.

In your experience with AWS Lambda, what’s been the most challenging aspect of ensuring scalability in serverless architectures, and how did you overcome it?

One of the most challenging aspects of scaling serverless architectures—especially with AWS Lambda—is ensuring predictability at scale. Lambda gives you horizontal scale by default, but scaling without insight can be dangerous. The real challenge isn’t spinning up more executions; it’s ensuring that a system behaves predictably when hundreds of thousands of concurrent invocations interact with queues, streams, retries, and downstream services—each with their own limits and failure modes.

One memorable case involved a customer workload that was highly bursty and relied on chained event triggers. While the system scaled “beautifully” on paper, in practice, it created what I call amplified retries: a small failure in one component triggered exponential retries downstream. The result? Latency spikes, noisy dashboards, and eventually throttled services. Everything technically worked, but it worked unreliably.

To solve this, we had to move beyond brute-force scaling. I helped lead an initiative to introduce adaptive concurrency controls and smarter retry orchestration, including mechanisms for backpressure awareness, circuit breakers, and token-based admission. We also built simulation environments to model real-world traffic patterns and failure cascades, allowing us to test resilience before deploying.

This experience taught me that scalability in serverless isn’t about growing fast—it’s about failing gracefully and recovering predictably. The systems that scale best are the ones that are observably honest under stress.

Ultimately, overcoming these challenges wasn’t just about writing better code—it was about building better mental models. We shifted the conversation from “how fast can this scale?” to “how safe is this to scale?” and that has made all the difference.

You’ve emphasized the value of prototyping and validating assumptions. Can you describe a time when this approach led to an unexpected but beneficial outcome in your work?

One of the most surprising and rewarding outcomes of prototyping came when I began exploring whether large language model (LLM) inference could run effectively on AWS Lambda. Conventional wisdom said it wasn’t a good fit – too slow, too memory-intensive, too unpredictable. But rather than dismiss the idea outright, I decided to prototype a minimal setup that streamed responses from an external LLM endpoint using Lambda functions as the execution layer.

The early prototype was simple: a Lambda function calling an external model with a prompt and streaming the tokens back to the client through API Gateway. But as I ran different scenarios, I started uncovering patterns – things like API Gateway timeouts due to token latency, the cost of redundant context passing, and how cold starts distorted tail latencies in ways that traditional inference systems didn’t account for.

What began as a weekend experiment led to some powerful realizations. We introduced adaptive context caching, prompt deltas, and early token flushing to keep the user experience responsive. I also discovered that you could optimize cost and latency by dynamically shaping requests based on the model’s behavior, not just infrastructure limits.

These insights shaped not just internal architectures, but external writing and talks I gave later. They also seeded the core ideas for my Substack essays and IEEE research on LLM fairness and reliability in serverless environments.

The biggest lesson? Prototyping isn’t just a development phase – it’s a discovery engine. It gave me the freedom to ask “what if?” without risking production systems, and the data to challenge long-held assumptions with evidence, not intuition.

Sometimes, the most impactful changes begin when you’re just playing with an idea no one else is ready to believe in.

How do you approach the balance between innovation and stability when introducing new technologies in large-scale systems?

Well, this is a really interesting question—and honestly, one that comes up a lot in my day-to-day work. When you’re operating at the scale of AWS, where millions of customers rely on the infrastructure to just work, stability is absolutely non-negotiable. But at the same time, if you don’t keep evolving, even the most stable system becomes a liability over time.

For me, the key is to treat innovation and stability not as competing forces, but as different aspects of thoughtful engineering. It’s less about finding a perfect middle ground and more about knowing when and where to take risks.

I usually lean on three guiding principles:

1. Isolate risk before scaling it. Any time we’re introducing a new mechanism—say, a smarter retry model or a GenAI-specific optimization, we sandbox it. We roll it out in stages, behind feature flags or as shadow traffic, and only promote it to wider usage after we’ve seen how it behaves under pressure.

2. Build for observability first, innovation second. One of my go-to mantras is: if you can’t measure it, you can’t trust it. Before we innovate, we make sure the system has the right telemetry, logging, and introspection hooks to give us feedback. It’s not just about collecting metrics—it’s about understanding the story the system is telling us.

3. Start with boring, then get clever. And finally—this may sound counterintuitive—but we often start with the simplest, most “boring” solution that works. Only after we see real usage and friction points do we layer in optimizations. That’s how ideas like adaptive LLM token orchestration or event-aware retry policies came to the forefront – they evolved from simple, well-tested beginnings.

At the end of the day, responsible innovation is reversible, observable, and rarely flashy in its first version. That mindset has helped me introduce meaningful change without ever putting reliability on the line.

In your opinion, what’s the most underrated skill for tech professionals today, and why do you think it’s crucial for long-term success?

That’s a great question, and I think one of the most underrated skills today is pattern recognition over time. In tech, we often celebrate people who can quickly learn a new framework or ship a clever solution. But what sets long-term builders apart is the ability to spot recurring themes—how small trade-offs today echo into bigger challenges months later. Pattern recognition helps you avoid repeating the same mistakes across different systems, teams, and even generations of architecture.

For example, I’ve seen retry storms, observability blind spots, and cache-invalidation issues resurface in almost every large-scale system, just in slightly different clothing. Engineers who develop the ability to say, “I’ve seen this movie before, and here’s how it ends,” are the ones who bring calm during chaos and design with foresight rather than hindsight.

This skill isn’t flashy. You won’t get applause for preventing a problem that never occurred. But over time, pattern recognition is what allows you to scale your judgment, to not just build things that work, but systems that evolve well.

And in an era where technologies change every few years, this kind of timeless thinking is more valuable than ever.

You’ve worked on simplifying event-driven architectures. What do you see as the next big challenge in making complex systems more accessible to developers?

Very timely question. We’ve made great strides in abstracting infrastructure—developers today don’t need to think about provisioning servers or managing threads to build something powerful. But the next big challenge is reducing cognitive latency: the gap between what a developer wants to express and what the system needs to function reliably.

Event-driven architectures offer amazing scalability and decoupling, but they also introduce invisible complexity—timeouts, retries, ordering issues, and eventual consistency. These aren’t just implementation details; they fundamentally shape the mental model developers need to hold in their heads.

The next leap, in my view, is semantic simplification without sacrificing control. We need tooling that understands intent, not just code orchestration but architectural intent. Think: systems that can simulate failure modes, recommend optimal retry strategies, or explain why a message was reprocessed five times before landing. This becomes even more critical as we integrate GenAI, where inference latency and token unpredictability clash with traditional event assumptions.

In short, the goal isn’t just to hide complexity—it’s to surface the right complexity, at the right time, in the right way. When we do that, we don’t just make systems accessible—we make developers more powerful.

Looking ahead, what emerging technology or trend do you believe will have the most profound impact on the tech industry, and how are you preparing for it?

Without a doubt, I believe the most profound impact will come from the convergence of Generative AI and real-time, adaptive infrastructure. We’re entering a phase where it’s not enough to just build intelligent applications—you need infrastructure that can reason, respond, and optimize alongside them.

LLMs are fundamentally different from traditional workloads. They’re bursty, opaque, and often resource-intensive in unpredictable ways. But today’s infrastructure—especially serverless systems—wasn’t originally built with that behavior in mind. This mismatch is creating a new class of challenges around latency management, prompt orchestration, caching strategies, and cost control.

What excites me is the opportunity to flip the model: what if the infrastructure itself became intelligent? Imagine systems that can dynamically choose the best model endpoint, prefetch likely prompts, or throttle non-critical invocations based on real-time user behavior and model load.

To prepare for this shift, I’m working on systems that treat infrastructure decisions as programmable layers, not static configs. I’ve been prototyping adaptive LLM workflows, writing about retry fairness, and pushing for smarter observability into GenAI pipelines. I’m also investing time into cross-disciplinary thinking—how insights from compiler theory, game theory, and human factors can shape the next generation of intelligent platforms.

The future won’t be defined by just faster models or fancier APIs. It’ll be defined by infrastructure that’s aware, responsive, and built to co-evolve with AI. That’s where I’m betting—and building.

Thanks for sharing your knowledge and expertise. Is there anything else you’d like to add?

Just this: in tech, it’s easy to get caught up in tools, frameworks, and abstractions. But the systems we build—and the careers we shape—are ultimately about people. The best engineering decisions I’ve made weren’t just clever—they were clear, sustainable, and made life easier for someone down the line.

Whether it’s simplifying serverless architectures, scaling GenAI workflows, or debugging the edge cases no one sees coming, I’ve learned that engineering is less about perfect solutions and more about resilient conversations with complexity. My goal is to keep making that conversation a little more human, a little more observable, and a lot more scalable.

If you’re thinking deeply about infrastructure, AI, or building systems that last, I’d love to connect and learn from each other. You can find me here: linkedin.com/in/rajeshpandeyiiit