AI Product Manager - India

About this role

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Skills you'll use

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

Who can apply

• 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
• 2-5 years of product management experience owning a roadmap end-to-end, from strategy through shipped outcomes
• Direct experience shipping AI/LLM-powered features in production, including owning evaluation and quality decisions for them
• A real, hands-on feel for what different LLMs and model families are good and bad at, and the ability to prototype against that understanding rather than treating "AI" as a black box
• Real technical depth: comfortable in architecture discussions, and able to reason about tradeoffs (model choice, latency, cost, deterministic vs. LLM-based logic) as a peer to the engineers you work with
• Comfort defining and reasoning about eval sets and failure modes, and treating quality as a property of a non-deterministic system rather than a binary pass/fail
• Experience partnering cross-functionally with Sales, Marketing, and Customer Success around releases to connect product decisions to business growth
• A high ownership mindset: treating outcomes, not deliverables, as the responsibility

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