AI Product Manager
New
D
DscoutUX Research Technology
Remote - IndiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 2-5 years
- Required Skills
- Artificial IntelligenceProduct ManagementPrototypingLLM
Requirements
- 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
- Hands-on feel for LLMs and model families with the ability to prototype against that understanding
- Real technical depth: comfortable in architecture discussions and reasoning about tradeoffs like model choice, latency, and cost
- Comfort defining and reasoning about eval sets and failure modes
- Experience partnering cross-functionally with Sales, Marketing, and Customer Success around releases
- High ownership mindset: treating outcomes, not deliverables, as the responsibility
- Experience with AI tools like Codex, Claude, OpenClaw, or Cursor
- Experience shipping features within strict privacy, security, and compliance constraints
- Nice to have: experience with LLM observability/eval tooling (e.g., Braintrust, LangSmith)
- Nice to have: background in a technical role (engineering, data science) before moving into product
- Nice to have: experience in B2B, ideal if a market adjacent to UX/market research or insights/feedback
Responsibilities
- 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
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