AI Product Owner - W2 Only
Job Summary
Define and own the product vision, strategy, and roadmap for agentic AI solutions aligned with business objectives. Identify high-value use cases where autonomous agents can drive efficiency, automation, or new capabilities. Evaluate build-vs-buy decisions across agentic AI frameworks (LangChain, LangGraph, AutoGen, CrewAI) and LLM providers. Stay current on the evolving agentic AI landscape and translate emerging capabilities into product opportunities.
Backlog & Requirements Management
Own and prioritize the product backlog, translating business requirements into clear user stories and acceptance criteria for engineering teams. Define agent behaviors, decision boundaries, and success criteria (task completion rates, accuracy, latency, cost per task). Specify requirements for agent memory, tool access, multi-agent orchestration, and human-in-the-loop checkpoints. Balance autonomy vs. control determining where agents should act independently vs. escalate to humans.
Cross-Functional Collaboration
Act as the primary liaison between business stakeholders, AI/ML engineering teams, UX designers, and data governance/compliance teams. Partner with engineering to scope technical feasibility, effort estimation, and architecture trade-offs for agentic workflows. Work with data science/ML teams to define evaluation frameworks for hallucination rates, task success, and agent reliability. Collaborate with design teams on human-agent interaction patterns and interfaces (chat, dashboards, approval workflows).
Governance, Risk & Compliance
Define guardrails, escalation paths, and fallback mechanisms for agent failures or edge cases. Ensure agentic systems meet responsible AI standards: transparency, auditability, bias mitigation, and data privacy. Partner with legal/compliance teams on risk assessment for autonomous decision-making systems. Establish monitoring and audit trails for agent actions, especially in regulated domains.
Launch & Performance Management
Drive go-to-market planning, pilot programs, and phased rollouts of agentic AI features. Define and track KPIs/OKRs: task automation rate, agent accuracy, cost savings, user adoption, time-to-resolution. Analyze production agent performance data to identify improvement opportunities and prioritize iterations. Manage stakeholder communication, demos, and executive reporting on agentic AI product progress.
Agile Delivery
Prioritize technical debt, model updates, and framework migrations alongside feature development. Manage dependencies across multiple agent workstreams and cross-functional teams.