Sr. Technical Product Manager, AI Platform - Hybrid (Lake Oswego, OR)

About the Role: You'll own the roadmap for an enterprise AI platform, including the primitives, runtime, orchestration, and governance that product teams build on top of. This is platform product management, so your customers are the application teams and AI product managers who depend on what you ship. Our client is an established software company in governance, risk, and compliance. They're building a shared AI platform that powers agent-driven capabilities across their product portfolio, and this role sits at the center of it, deciding what becomes a reusable platform service and what stays in the application layer.

What You'll Do: Define and evolve the multi-horizon roadmap for the AI platform, spanning primitives, runtime infrastructure, orchestration, governance, and release controls Turn platform strategy into prioritized capability requirements and delivery milestones tied to adoption and scale Lead execution so roadmap priorities ship as reliable, production-ready platform capabilities, not one-off features Partner with application teams and AI product managers to translate workflow needs into reusable platform services Define requirements for AI runtime behavior, including execution reliability, scaling, tenant isolation, context assembly, and policy-aware interactions Establish orchestration primitives, intent interpretation frameworks, and safe tool-invocation patterns that application teams can build agent-assisted experiences on Set observability, evaluation, and governance standards, including execution tracing, telemetry, auditability, and evaluation-gated releases Drive alignment across engineering, security, compliance, data, and product so capabilities scale safely across the portfolio

Qualifications: 8+ years in technical product management, with at least 3 years in platform, infrastructure, or AI/ML environments A track record of delivering platform products used by multiple product teams or services Strong understanding of LLM-based and agentic systems, including runtime design, context assembly, orchestration patterns, and tool-using architectures Comfort defining measurable success criteria for non-deterministic AI systems Familiarity with AI governance and risk management principles in enterprise environments Experience coordinating cross-functional delivery across engineering, product, security, and compliance teams A clear communicator who can turn complex technical systems into product and executive narratives