NeuralLens ↗
Real-time defect detection for manufacturing lines — 40 ms inference at 99.2% recall across 14 product variants.
// hello world — i build systems that learn
● available
I design, train and ship production-grade ML systems — from research notebooks to models serving millions of predictions a day. Less hype, more shipped.
// about
I'm a machine learning engineer who lives in the gap between research and reality — where a promising paper becomes a model that survives real traffic, real edge cases and real 3 a.m. pager alerts.
Over the last six years I've built vision systems that inspect factory lines, speech models that run on-device in 12 Indian languages, and retrieval pipelines that make enterprise search actually find things. My happy place is squeezing latency out of inference and watching a loss curve finally behave.
When I'm not training models, I'm writing about ML systems, mentoring early-career engineers, and over-engineering my espresso routine.
$ whoami → curious · rigorous · shipped
// selected work
Four projects that best show how I think — each one taken from idea to production.
Real-time defect detection for manufacturing lines — 40 ms inference at 99.2% recall across 14 product variants.
On-device speech recognition in 12 Indian languages — a distilled Whisper running offline at 4× real-time on mid-range phones.
Open-source model monitoring that catches data drift before your users do. 2.1k GitHub stars, used by 40+ teams.
Enterprise RAG platform answering questions over 2M+ internal documents — hybrid retrieval, reranking, and honest "I don't know"s.
// experience
// contact
Have a model to ship, a pipeline to fix, or just want to argue about transformers vs. state-space models? My inbox is open.
usually replies within 24h · or find me on LinkedIn