AI Product Manager - India

At Dscout, we’re building the most flexible and powerful UX research platform on the market—trusted by the world’s top brands in finance (JP Morgan Chase, Intuit, Charles Schwab, PayPal), healthcare (Aya, Headspace), consumer goods (Keen, Verizon, Target, Northface), and tech (Google, Amazon, Facebook, Meta, Spotify, AirBnB). Our tools help teams deeply understand the humans behind their products, so they can build better ones. We are expanding our smart and driven team and would love for you to join us. We're looking for an AI Product Manager who brings the full standard PM toolkit — user research, market and competitive analysis, roadmap and strategy, cross-functional delivery, and strong collaboration instincts - and applies it to products where the model underneath doesn't behave the same way twice. You have a real, hands-on feel for what different LLMs are actually good and bad at, and you use that to prototype ideas yourself, sometimes shipping small, production-quality AI features directly. You default to ownership: of the roadmap, of outcomes, of the quality bar that decides whether something's actually ready to ship, and of what an agent should be trusted to do on its own versus when a human needs to stay in the loop. That's because building AI-native products means quality is a distribution, not a pass/fail — a feature can work correctly most of the time and still need a real answer for the failure tail, since the underlying system is non-deterministic, not just complex. What you'll do Lead the roadmap and strategy for your product area, from problem discovery through delivery and post-launch iteration Lead the evaluation bar for the model-powered surfaces you're responsible for: define eval sets, identify failure modes, and know the rollback plan before a change ships Prototype product ideas directly using your own understanding of model strengths and weaknesses, and ship small, production-quality AI features yourself when that's the fastest path to learning Lead product decisions about agent autonomy: what the agent should be trusted to decide and act on independently, what needs a human in the loop, and how that line should move as trust in the system grows Treat prompting and context design as a product lever you use directly to shape behavior Partner with engineers on technical architecture with enough depth to challenge assumptions, propose alternatives, and influence design decisions Partner with Design and Research on UX so features are genuinely usable and understandable, not just technically correct Partner with Sales, Marketing, and Customer Success around releases — shaping GTM messaging, training, and rollout, and closing the loop on adoption signal afterward Track the metrics that actually matter for a non-deterministic system — quality/accuracy distribution, latency, cost-per-task — alongside the usual adoption and growth metrics What you bring