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The AI Center of Excellence builds shared AI building blocks - primitives - that change how Amazon's systems work, not what features they ship. We take on the hardest problems first, prove each primitive with one partner on one real problem, then open it up so any team at Amazon can adopt what we've validated without solving it again.

We are looking for a Product Manager - Technical who can own the vision and lifecycle of these primitives, drive them from concept through adoption, and make sound product and technical trade-offs in a fast-moving AI domain.

Key job responsibilities
- Own the product lifecycle for AI primitives end-to-end - from customer discovery and problem framing through vision definition, roadmap prioritization, execution with cross-team dependencies, launch, and post-launch performance optimization

- Define product strategy by understanding problems that cut across multiple teams and personas - translating that into clear principles, requirements, and roadmap priorities that sequence work on evidence

• Drive adoption by partnering with early adopters, generalizing requirements from multiple contexts, and ensuring primitives work for any team without requiring your team to operate them

- Evaluate technical proposals alongside scientists and ML engineers - understanding architecture trade-offs and balancing feature delivery with system health and long-term maintainability

- Make crisp product decisions in ambiguous AI/ML domains where the problem and strategy may not yet be defined - using data, customer insight, and judgment

- Define success criteria that measure the primitive you own, not the partner's business outcome you don't - distinguishing adoption from real impact

- Ensure primitives are findable, adoptable, and valuable - not just built and opened

- Contribute to the team's rhythm of business - planning cycles, business reviews, goal tracking, and operational reporting

A day in the life
Every day brings new challenges. You might work backward from a partner team's pain point to frame a new primitive, review a technical design with scientists to assess whether an architecture can scale, or analyze adoption data to decide what to double down on versus deprecate. Other days, you'll write a vision document for a new capability, find new opportunities across the enterprise, or sit with early adopters to understand how they'd consume a primitive in their pipeline. You'll move between technical discussions with scientists and engineers and alignment conversations with senior managers and directors.

No two weeks look the same. You'll move between customer discovery, technical strategy, and hands-on execution - always building toward primitives that are trusted, consumable, and impactful at scale.

About the team
AICE is a team of scientists and machine learning engineers building the AI primitives that power Amazon's intelligent systems. We develop and harden reusable capabilities for broad consumption by product teams across the enterprise - and partner with those teams to integrate them.