9+ years turning frontier research into 39 end-to-end AI systems across Generative AI, Agentic AI, LLMs, NLP and Vision — at KLA, Amazon, Oracle, Rakuten & Cisco. Now fusing deep technical craft with a STEM MBA to scale AI into business value.
I am an ML Scientist and technical leader with 9+ years spent at the frontier — shipping 39 highly scalable, end-to-end AI systems from research to production. My work spans Generative AI, Agentic AI, multimodal LLMs, NLP/NLU, computer vision, recommendation and reinforcement learning.
At KLA I built AURORA, a 10-agent autonomous system that turns bug reports and feature requests into production-ready pull requests in ~6 minutes — shipping 120+ PRs across 9 repositories with zero human intervention. At Amazon, as Principal Applied Scientist, I led a 15-person cross-functional team to build LLM-powered personalization that lifted CTR 44% and dwell time 53%, and authored the science roadmap that VPs approved for 2024.
I don't just build models — I build teams and translate ambiguity into strategy. I've mentored 45+ engineers, secured VP-level buy-in for 12 strategic initiatives, and owned multi-quarter roadmaps across Retail, Devices, Prime Video and AWS. My STEM MBA at Ohio State (Fisher Leadership Fellow) sharpens the bridge between technical depth and P&L outcomes.
Underneath it all is a lifelong obsession with excellence: Rank 1 out of 1 million in my 12th board exams, Rank 1 in Computer Science at BITS Pilani, and top-1% national finishes in Physics, Chemistry, Math and Astronomy olympiads.
From humanoid brain-computer interfaces to autonomous multi-agent engineering systems — across six world-class organizations.
Ten production systems I architected and shipped end-to-end — from the research and the model to the infra, the tests and the P&L impact. Every metric below is drawn straight from delivery.
A self-correcting, multi-agent software factory that ingests bug reports & feature requests and returns production-ready pull requests — orchestrating triage, routing, code generation, review and verification loops with zero human in the loop.
Twin agentic-RAG assistants with hybrid retrieval (BM25 + MPNet dense embeddings + semantic re-ranking) and a Gemma dual-model router, delivering streaming answers with voice input — engineered for near-zero latency at zero infrastructure cost.
A natural-language wiki-editing agent: describe the change in plain English and it plans, drafts and applies it — with explainable-AI confidence scoring, NER extraction, semantic-coherence checks, multi-page batch editing and inline diff previews before anything is committed.
Production-grade, label-based role-based access control securing a knowledge graph across five databases — 500+ custom roles enforced with O(1) bitmap authorization that holds at unbounded scale, backed by a full automated test suite.
An automated migration pipeline that moved a 75-page, 84-attachment Azure DevOps wiki to GitHub — converting syntax, redacting secrets, and surfacing critical credential leaks that were live in the source content.
Four production Claude Code skills published to KLA's internal AI registry, embedding compliance and best practices directly into the developer workflow — from first install to code review.
A session-aware, sequential recommendation agent powered by LLMs — built with a 15-person cross-functional team across the Ads & Prime Video orgs to deliver zero-shot personalization in real time.
A lightweight multimodal model accelerating product mapping, ranking and catalog de-duplication across Kindle & Prime Video for 84 external competitors on 3 continents — outperforming every CNN baseline on both accuracy and efficiency.
An enterprise RAG system for AWS Documentation plus Sherlock, a hybrid BERT + ELECTRA product-matching model — together streamlining real-time query answering and slashing catalog onboarding time.
A production multi-agent system automating customer queries for 70M Amazon Pay customers — combining intent classification, chat, code-generation and function-calling models, and accelerating multi-hop Bedrock workflows 3×.
From the math and the model to MLOps, infra and the boardroom.
I write about the systems-level realities of agentic AI, LLM infrastructure and the future of GenAI products.
Whether it's an autonomous agent, a GenAI product, or an AI strategy that moves the P&L — I'd love to talk.