Making AI work in the real world

Ajay is a Machine Learning Engineer and researcher focused on LLM inference, ML systems in production, and performance optimization, with over five years of experience building and scaling AI products at B2B startups.

A large part of that work involved edge-based computer vision deployments with hard hardware limits, distributed camera fleets, and the kind of operational failures that do not show up in tidy benchmark papers. That experience now informs a broader interest in inference systems, serving constraints, and model efficiency across modern ML workloads.

Current work at Rochester Institute of Technology builds on those production constraints, with emphasis on efficient inference, deployment-aware system design, and making advanced models practical in production environments.