Senior Digital Product Manager, Product Operations - Tech Enablers & PDLC

About the role

Amgen is hiring a Senior Product Manager, Product Operations - Tech Enablers & PDLC, reporting to the Director, Product Operations. This individual contributor role owns the tools, platforms, AI agents, workflows and Product Development Lifecycle (PDLC) infrastructure that enable product teams to practice modern product management. It supports the rollout of the Digital Product Operating Model (DPOM), with a short-term focus on an AI-assisted PDLC and a longer-term vision of an AI-led PDLC.

Key responsibilities

• Own product requirements, evaluation and adoption support for tools and platforms that enable the DPOM and modern product management
• Evaluate tools, platforms, AI agents and workflows covering strategy, discovery, roadmapping, OKRs, prioritization, intake, requirements, stakeholder alignment, delivery, measurement, documentation and lifecycle management
• Partner with product teams to turn their needs into requirements, pilots and adoption plans
• Help define and enable the short-term AI-assisted and longer-term AI-led PDLC, and experiment with AI-enabled tools and workflows
• Improve the tools, workflows and practices across the Product Development Lifecycle and partner with Product Operations teammates so they align with DPOM standards
• Lead adoption support including pilots, onboarding, training and feedback loops, and partner with Product Management Hub, Agile Enablement, Enterprise Architecture, AI teams and others
• Embed with product teams as a hands-on coach, teaching core product management skills such as problem definition, discovery, strategy, roadmaps, OKRs, KPIs and prioritization
• When no DPM is assigned, temporarily perform product management responsibilities such as shaping product direction and supporting roadmap and prioritization decisions
• Help teams adopt DPOM practices, clarify roles and decision rights, and build durable product habits
• Support product strategy, outcomes and prioritization, including trade-off recommendations and discovery, validation and learning practices