Product Management with Generative & Agentic AI

A six-month executive program at BITS School of Management focused on the full product lifecycle for GenAI and agentic AI: market and user discovery, problem framing, PRDs and road-mapping, prioritization, pricing and go-to-market, and post-launch measurement. The coursework treats AI products as systems, covering model selection tradeoffs, evaluation, safety and guardrails, and the economics of inference alongside classical product craft.

The program is build-heavy. Every framework was applied to a real product that had to ship: I designed, built, and delivered a voice-first financial copilot (Saarthi) end to end, as its solutions architect, AI engineer, and product manager, taking it well past prototype into a pilot-ready production application. That end-to-end ownership is what I carry into forward deployed work: sitting between the customer's business problem and the engineering, writing the spec, defending the tradeoffs, and shipping the thing.

What it adds on top of my engineering track record is structured product judgment: JTBD-driven discovery, MoSCoW and RICE prioritization, success-metric trees tied to activation and retention rather than vanity counts, and SWOT-grounded positioning. It is the difference between building what was asked and building what moves the customer's metric.