About
My Journey
My path into AI started with a deep fascination for how machines learn from data. Early in my career, I built ML models for NLP and information retrieval problems, learning to navigate the gap between research benchmarks and real-world complexity. That tension—between academic rigor and engineering pragmatism—has defined my approach ever since.
Over 12+ years, I evolved from applied researcher to senior AI leader, shipping production systems used by millions across Alexa, AWS, and enterprise GenAI platforms. The transition from writing code to guiding teams required learning a different craft: how to set technical direction, build research culture, and translate long-horizon bets into near-term milestones.
Today at Amazon, I lead the research and development of Amazon Nova— AWS's flagship foundation model family. My work spans the full LLM lifecycle: architecture decisions, post-training (SFT, RLHF, GRPO), reasoning and planning, and autonomous agentic systems. I collaborate with scientists, engineers, and product leaders to ship AI that is both frontier-capable and production-reliable.
I'm a published researcher at ACL, EMNLP, NeurIPS, CoRL, and ECML, and I believe that staying close to the research frontier is what keeps engineering decisions grounded in what is actually possible—not just what is currently deployed.
Experience
Senior AI Leader & Applied Scientist
Amazon (AWS AI) · Present
Leading 20+ scientists on Amazon Nova foundation models and AWS AI Agents. Full LLM lifecycle from architecture to deployment.
Applied Scientist
Amazon (Alexa AI) · Previous
Built production NLP and ML systems shipped to millions of Alexa users worldwide.
Skills & Expertise
Foundation Models
Agentic AI
Reinforcement Learning
NLP & Research
Leadership Philosophy
Think in Systems, Act Strategically
Great AI leadership is about setting a direction that outlasts any individual contribution. I invest heavily in defining multi-year research roadmaps that align to real business outcomes, while leaving space for the discoveries that only emerge through experimentation.
People First, Results Follow
The best research cultures are built on psychological safety, clear ownership, and a shared sense of mission. I try to create environments where scientists feel free to take risks, challenge assumptions, and grow faster than they would anywhere else.
Research to Production is a Discipline
Translating a research idea into a production system that serves millions of users requires a different kind of rigor than publishing a paper. I have learned to build that bridge deliberately—through careful system design, robust evaluation, and tight feedback loops between research and engineering.
Stay Technical, Stay Curious
I believe leaders in AI must remain technically grounded. I stay involved in architecture reviews, read papers regularly, and maintain hands-on familiarity with the systems my teams build. Long-term thinking in AI requires knowing what is on the frontier today.