I hold a B.E. in Electrical and Electronics from RV College of Engineering, Bangalore, and an M.S. in Computer Science from the University of Florida.

My work sits between full-stack software engineering and applied machine learning. I have built production systems for large-scale, real-world use cases, and I have designed and optimized AI systems with the same emphasis on reliability, performance, and security. Experience in enterprise compliance settings further shaped how I approach design: solutions should be technically robust, measurable, and fit for the environments in which they run.

In practice, that has included scalable evaluation pipelines and benchmarks for vision-language models, energy-efficient LLM inference, production-grade NLP applications, and multi-agent systems with forecasting and retrieval-augmented generation. At the UF Data Studio lab, I worked on domain generalization and adversarial robustness, asking not only whether a model scores well, but whether it remains trustworthy under distribution shift and attack. Across these efforts I care about clean, reproducible code and improvements that can be measured rather than assumed.