AI Product Manager
Summary
Own AI products end-to-end, from scoping to shipping, using LLMs, agents, and automation to build AI-native experiences like chatbots and copilots for global users.
- Own AI products end to end, from discovery and scoping to prototyping, shipping, measuring, and iterating in production.
- Identify high-impact opportunities across player-facing systems, operational workflows, and internal tools, and decide what is worth building.
- Determine the right technical approach for each problem, whether rules, classical ML, LLMs, agents, or hybrid solutions, and discuss tradeoffs with engineers across prompting, RAG, fine-tuning, model selection, tool calling, latency, and cost.
- Design AI-native experiences such as chat, copilots, human-in-the-loop flows, fallbacks, and feedback loops, and define the evaluations that determine whether they work.
- Prototype in code using tools such as Claude Code or Codex, building working demos before writing specifications so that engineering discussions begin with something tangible.
- Manage stakeholders across leadership, engineering, and end users to align priorities and drive adoption.
- Work directly with users, observe how they interact with shipped products, debug issues on the spot, and translate learnings into the next iteration.
- At least 2 years of full-time experience in product management, startup, or founder-like roles, with a track record of taking products from 0 to 1.
- Builder mindset, with a preference for shipping a rough prototype quickly over delivering a polished specification later, and the willingness to use code, no-code, or AI tools to do it yourself.
- Experience implementing LLM products or enterprise AI applications, with leading product directions through Chat, Copilot, RAG, AI Agents, automated workflows, internal operations tools, or enterprise productivity platforms, covering all processes from PoC to launch or scaled rollout.
- Comfort with the uncertainty of AI systems and the instinct to engineer toward reliability through evaluations, fallbacks, and human-in-the-loop design.
- Strong product sense for turning ambiguity and complexity into simple, trustworthy product experiences.
- Open-minded, agile, and proactive mindset with a strong willingness to learn.
- Strong sense of responsibility and accountability in delivering quality work on time.