Can you introduce yourself and tell us about your current role in the tech industry?
I’m Rajesh Kumar Pandey, a Principal Engineer at AWS, where I focus on building resilient serverless architectures and large-scale distributed systems. My work centers around designing scalable event-driven workflows, optimizing cost-performance trade-offs, and engineering for reliability at scale. Over the years, I’ve led efforts that power some of the most critical async workloads in the cloud, authored patents on high-availability orchestration techniques, and helped evolve how teams think about GenAI deployment in serverless environments. Outside of my role, I contribute actively to the broader tech community through writing, speaking, and mentoring, particularly in the areas of cloud computing, event-driven systems, and emerging infrastructure for AI.
What inspired you to pursue a career in technology, and how has your journey evolved to where you are today?
Growing up, I was always fascinated by how systems worked, from basic electronics to early computer programs. What hooked me was the realization that technology isn’t just about machines; it’s about building tools that can solve real-world problems at scale. That curiosity led me to pursue computer science and, eventually, to roles that allowed me to design and optimize complex distributed systems.
My journey has taken me from writing code for monolithic systems to architecting global-scale serverless platforms at AWS. Along the way, I’ve embraced both the technical and human sides of engineering, whether it’s simplifying reliability in asynchronous systems, helping teams ship resilient cloud-native services, or mentoring others navigating the same path. Today, I’m particularly excited about how emerging fields like Generative AI are intersecting with infrastructure and reshaping what’s possible in real-time, event-driven applications.
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?
Absolutely. One of the most pivotal moments came during a large-scale migration project at AWS where we were transitioning a critical workload to a serverless architecture. Midway through execution, we encountered an unexpected bottleneck: the standard retry mechanisms were silently amplifying downstream failures, causing erratic behavior and making observability a nightmare. Rather than doubling down on the original design, we paused, re-evaluated the assumptions, and built a custom backpressure-aware retry orchestration model tailored for event-driven workloads. This adaptive approach not only stabilized the system under load but also laid the foundation for some of the reliability patterns we later formalized across the platform. That experience reinforced something I now carry into every project: in modern systems, adaptability isn’t a soft skill, it’s a survival strategy.
In your experience with AWS Lambda, what’s the most challenging aspect of ensuring high performance and scalability in serverless architectures?
The hardest part isn’t raw scalability. Lambda scales impressively by default. The real challenge lies in orchestrating predictability in performance across highly dynamic, asynchronous workflows. Serverless environments abstract away infrastructure, but that abstraction can obscure critical pressure points—like burst concurrency limits, cold starts, or event fan-out bottlenecks.
One specific complexity is coordinating retries and timeouts in distributed chains. A misconfigured retry policy in one service can cause cascading amplification, silently compounding latency and cost. Another is maintaining consistent performance when invoking heavyweight workloads like Generative AI models, where token latency and model cold starts behave nothing like traditional APIs.
To address these, we’ve had to go beyond auto-scaling, building intelligent admission controls, adaptive token budgeting, and resilient fallback paths that make Lambda not just scale more, but scale smart. That’s where serverless becomes less about “infinite scale” and more about engineering discipline.
You’ve emphasized the value of prototyping and validation. Can you describe a time when this approach helped you navigate a particularly risky or innovative technology decision?
One standout example was when we began exploring how to run large language model (LLM) inference workflows on AWS Lambda. At the time, conventional wisdom said serverless wasn’t a good fit for GenAI due to latency, memory constraints, and execution time limits. But rather than dismiss the idea, we built a lightweight prototype that streamed token outputs from an external model endpoint using adaptive context injection and prompt-delta techniques. Through that prototype, we quickly validated what would and wouldn’t work, like the need for early flushing to avoid API Gateway timeouts, and the critical role of caching prompt scaffolds. That early validation saved months of potential misdirection and gave us the confidence to invest in a full-fledged, production-grade architecture that could handle scalable, real-time GenAI inference using Lambda. That experience reinforced why I treat prototyping not as a phase, but as a mindset, especially when charting unknown territory.
How do you approach mentoring or guiding less experienced engineers in developing a long-term perspective on technical decisions?
I try to shift the conversation from “what works now” to “what breaks later.” Early in their careers, engineers often focus on getting the feature shipped, and understandably so. But I help them ask deeper questions: What happens under load? Who maintains this in two years? How will this integrate with other evolving systems?
One technique I use is walking them through the second-order consequences of decisions, like how a seemingly small choice in data partitioning can create hotspots or how retry logic can silently overwhelm downstream services. We also review real postmortems and scaling inflection points together to develop intuition for system evolution.
Ultimately, I want them to see architecture not as a fixed diagram, but as a living organism, one that grows, decays, and needs to be designed with change in mind. The goal is to help them build not just systems, but judgment.
You’ve worked on event-driven architectures. What’s a common pitfall you’ve observed in implementing these systems, and how can engineers avoid it?
One of the most common pitfalls is treating events as fire-and-forget messages without designing for observability and failure recovery. In event-driven systems, failures don’t shout; they whisper. An event might get lost, a consumer might silently retry for hours, or a downstream service might partially fail without triggering alarms. Without strong visibility into the event flow and lifecycle, these issues can go undetected until they cause real damage.
To avoid this, engineers need to think of event-driven systems as distributed state machines. That means incorporating idempotency, DLQs (Dead Letter Queues), and traceable event metadata from day one. Also, investing in tools that let you see events as they move, whether via tracing, structured logs, or custom dashboards, turns black boxes into something debuggable.
Event-driven architecture can be incredibly powerful—but only if you treat visibility and resilience as first-class citizens, not afterthoughts.
Can you share an example of a personal tech project that unexpectedly benefited your professional work, similar to your simulator experience?
Yes, one example that stands out is a personal experiment I built to simulate event stream failures and retry patterns using synthetic workloads. Initially, it was just a weekend project to visualize how async systems degrade under stress, what happens when one component slows down, when retries kick in, and when queues start to back up. But it quickly turned into something far more valuable. I began noticing patterns, like how poorly tuned retry logic can cause cascading latency or how certain queueing strategies (like FIFO vs. LIFO) perform under different back-pressure scenarios. Those insights fed directly into my work at AWS Lambda, where we needed better mental models and tooling for async resilience at scale. What started as a curiosity-driven side project eventually helped inform architecture reviews, internal tools, and even public talks. It reminded me that sometimes the best R&D starts when you’re just trying to understand something for yourself.
Looking at the current tech landscape, what area do you think is ripe for innovation but often overlooked by both startups and established companies?
I think infrastructure-aware AI orchestration is massively underexplored. While there’s a rush toward building LLM-powered apps, very few teams are thinking about how to intelligently route, scale, and adapt these workloads at runtime, especially in cost-sensitive, latency-critical environments like serverless.
Everyone talks about fine-tuning or agent design, but almost no one is innovating on how these models are invoked across heterogeneous compute tiers, how prompts are cached and reused, or how inference workflows adapt to real-world infrastructure signals like cold starts, network jitter, or token latency.
There’s a huge opportunity here, what I call “AI-aware infra, and infra-aware AI.” The next generation of platforms won’t just call models; they’ll strategically negotiate with them, optimizing for performance, cost, and reliability in real time. That’s a space where innovation can shift the industry, but right now, most players are barely scratching the surface.
Thanks for sharing your knowledge and expertise. Is there anything else you’d like to add?
Just this: As engineers, it’s easy to get caught up in tools, trends, or performance metrics, but the real magic happens when we zoom out and design systems that are not just scalable, but understandable. Whether it’s a lambda function, a GenAI workflow, or an architecture diagram, clarity compounds over time, both for the systems we build and the people who work on them.
Also, I’m always eager to connect with others working at the intersection of distributed systems, serverless, and AI. Feel free to reach out on LinkedIn; let’s build things that last.
