Senior Product Engineer · AI Engineer · Tech Lead with 5+ years architecting enterprise-scale systems generating $50M+ in combined revenue, handling 200M+ API requests/month, and serving 150+ enterprise clients.
I'm a Technical Lead and AI Engineer who spends most days architecting distributed systems that survive real production load — banks onboarding millions of users, crypto desks indexing trillions of rows, AI agents classifying nine hundred thousand items overnight.
The work tends to look quiet on the outside: fewer demos, more uptime. Cleaner pipelines. APIs that used to take 94 seconds now take 2.4. Costs that used to scare CFOs, halved.
I've led engineering on products that have generated $50M+ in combined revenue, and I'm happiest when the org chart says “senior engineer” and the actual job is closer to resident systems thinker.
I've worked with engineers who use every AI tool available and still move slower than Nirbhay did before any of that existed.
The thing about Nirbhay is he doesn't treat engineering and product as two separate jobs. He'll be deep in the infrastructure one hour and then flag a UX problem in the next standup that the designer missed. We went from nothing to a working product in six weeks. Not a prototype — an actual product. Couldn't have asked for a better cofounder.
The frontend developer we had on the team was good but Nirbhay was moving at a completely different speed. Instead of slowing down or making it a management problem he just absorbed the work. Backend, frontend, didn't matter. He treated it like a problem to solve not a boundary to negotiate.
He once fixed a bug he found that wasn't in his scope, wasn't in his sprint, and nobody had reported it. Just saw it, fixed it, mentioned it in standup. Small thing. But it tells you everything about how he thinks about work. It's never just a ticket to him. It's a product that real people are going to use and a product he cares about.
He once fixed a bug he found that wasn't in his scope, wasn't in his sprint, and nobody had reported it. Just saw it, fixed it, mentioned it in standup. Small thing. But it tells you everything about how he thinks about work. It's never just a ticket to him. It's a product that real people are going to use and a product he cares about.
Leading, architecting and developing multiple enterprise systems from scratch, including ViddyScribe (Won Gemini App of the Year), Cerebellum Academy, Scuderia Car Parts, and Grayporter, generating combined revenue over 50 million USD.
Graduated as top of batch, receiving Gold Medal for academic excellence
Placed First in Hack-day, sponsored by Amazon-Alexa
President of Association for Computing Machinery (ACM)
Mentoring underprivileged kids for Devsnest Organization
From whiteboard to shipped product — full ownership across the stack.
Designing and shipping production-grade AI agents — tool use, MCP, retrieval, evals, and autonomous workflows.
Leveraging LLMs and AI tools for faster shipping cycles and higher quality output.
Designing distributed systems built to handle hundreds of millions of requests.
Production ML pipelines, real-time inference, and model serving at scale.
A 2026 operator playbook: when self-hosting wins, which inference engine to pick, the GPU buying matrix, quantization that actually works, and the four metrics you autoscale on.
The engineer-as-tech-lead view of product thinking — why most roadmaps are theatre, how the engineer gains leverage on what to build, and the discipline of writing your "no" list in public.
Notes from operating distributed systems at scale: where leverage lives, why root causes are a coping mechanism, and the architecture diagrams nobody draws but everyone runs.
Most production wins called "agents" are workflows with one tool-calling node. A field report on what actually ships, where the loops break, and why MCP — not the planner — was the real unlock.
