Bay Area, CA
Senior AI Leader building agentic AI systems, advocating for responsible AI practices, and mentoring the next generation of AI scientists.
I hold a PhD in Electrical and Computer Engineering from Texas A&M University, where my research applied Bayesian inference methods to genomic data - work published in venues including the Journal of Computational Biology. That path started earlier, in a bachelor's and master's in control engineering, working on signal processing fundamentals and dynamic control theory before graduate school layered in the ML theory and statistics. That combination of systems that have to behave predictably, and the math to reason about uncertainty still shapes how I approach production AI today: measured, evaluation-first, and skeptical of anything that can't be tested.
I led teams building production AI systems and fine-tuned LLMs for education at Chegg, and have lead AI science teams at Intuit, where I build personalized, agentic AI systems for finance. My work integrates cutting-edge agentic workflows with robust evaluation systems — driving business impact while keeping safety, reliability, and compliance non-negotiable.
I'm a strong advocate of responsible AI, mentor early-career AI scientists and engineers, and advise startups on AI strategy.
Agentic AI for FinTech
Led development of Production AI systems for EdTech
Texas A&M University, College Station
Indian Institute of Science, Bangalore
Why shipping an agent is the easy part, and evaluation is where the real engineering happens.
AlsoHow building safety and compliance into the architecture, not the approval process, changes what teams ship.
Open to conversations on agentic AI, responsible AI practice, and advising startups on AI strategy. The fastest way to reach me is email.