Build and improve production LLM agents and AI features through evaluation, experimentation, observability, prompting, context engineering, tool use, and workflow design. Develop backend services, APIs, data models, and feedback pipelines that make agent behavior reliable and measurable. Investigate performance issues, run staged rollouts and production replays, define quality standards, and partner with Product, Data Science, and Sales to deliver business outcomes with appropriate privacy, security, and human-oversight safeguards.

Own the AI product roadmap from discovery through launch and iteration. Define model evaluation standards, failure modes, rollback plans, and agent-autonomy boundaries. Prototype and ship production AI features, influence architecture, and apply prompting and context design. Collaborate with Design, Research, Engineering, Sales, Marketing, and Customer Success while tracking quality, latency, cost, adoption, and growth metrics.

Build and own full-stack product features from ambiguous problems through deployment. Partner with Product and Design, make UX decisions, integrate LLM and agent capabilities into trustworthy product experiences, talk with users, instrument releases, and iterate based on usage. The role requires strong product judgment, user empathy, independent execution, and comfort using AI coding tools while collaborating with AI engineers on model behavior.