Product leadership with the sleeves rolled up.

I'm Gaurav — a product leader based in Hyderabad, eighteen years into a career spent at the intersection of business strategy, AI fluency, and hands-on building.

I started in FinTech in 2007, translating trader pain points into features on an online trading platform. Since then I've built in EdTech, in logistics, in professional services, and now in enterprise HR SaaS. Different industries, one consistent problem: taking something the business needs but can't quite articulate, and turning it into something engineering can actually build.

Today I'm Senior Product Manager for Portfolio & AI Initiatives at iCIMS, where I orchestrate a $100M+ technical SaaS portfolio. My remit is deciding which AI investments deserve the company's engineering capacity — prioritising against unit economics, business-model depth, and genuine AI-driven ROI rather than how good something looks in a demo.

How I work

I prototype before I pitch. Cursor, Claude Artifacts, Replit, v0 — I build the rough working version myself, put it in front of real users, and bring evidence into the roadmap conversation instead of opinions. When I built an AI discovery agent to automate user-insight extraction, it wasn't to ship my code; it was to answer a question that analysis alone was never going to settle. It cut our discovery cycle from 60 days to 40.

I'm equally comfortable in the two rooms most product people pick between. I can sit with Data Science and Engineering on model behaviour, retrieval architecture, and MLOps — and then walk into an executive review and defend the business case with numbers a CFO recognises. That translation is most of the job.

The commercial track record I'm proudest of is Safexpress, where aligning technical feasibility with go-to-market strategy expanded ARR from ₹15 crore to ₹100 crore. Not because of one feature, but because product capability and commercial strategy finally pointed the same direction.

What I care about

AI products that move beyond hype and deliver something measurable. Data ethics and compliance treated as a design input rather than a late-stage gate. Teams where the fastest way to settle a disagreement is to go build the smallest possible version of it.

I gravitate toward ambiguous, high-growth environments — the ones where the roadmap isn't handed to you and the interesting work is figuring out what should be on it.

Beyond the day job

I write here about AI product management: prototyping culture, the economics of shipping AI features, and how discovery changes when synthesis stops being manual. I'm always open to conversations with people building in the same space — whether that's a collaboration, an advisory conversation, or a role where this kind of thinking is the point.

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