My work spans model training, regulated production ML, and enterprise agentic platforms. I lead architecture and delivery across GenAI evaluation, ML observability, and governed AI systems on AWS, Azure, and GCP, connecting technical decisions to measurable operating outcomes.
High-stakes AI fails between specialties. My experience reaches from model training through distributed delivery, evaluation, governance, and enterprise architecture.
At Tredence, I design and deliver agentic platforms, GenAI evaluation, ML observability, and multi-cloud AI architecture for enterprise clients, while mentoring multiple teams across the practice.
70% of migration work automated · 10× faster model onboarding · S&P 500 and global enterprise clients
At DEKA R&D, I built distributed ML and governed data systems across programs including Roxo, the FedEx SameDay Bot, and the FDA 510(k)-cleared twiist insulin delivery system.
80% less annotation work · 60% better root-cause accuracy · 43% fewer production bugs
The foundation includes an undergraduate internship at DRDO HEMRL applying ML to solid rocket propellant analysis, production ML inside Kafka and Spark systems at Horizon Geospace, and Faster R-CNN and LSTM research over 1,000+ videos at RIT.
Solid propellant analysis · Kafka and Spark · 1,000+ research videos
I created Agentic Engineering, a free, executable guide to building GenAI and agent systems at scale. It covers inference, RAG, evaluation, safety, durable execution, gateways, governance, cost engineering, and multi-agent systems.
I’m open to conversations about roles where architecture, implementation, and delivery all matter.