Senior Technical Program Manager, GenAI Games, Amazon Games GenAI & Strategic Initiatives

The Amazon Games GenAI team is seeking a hands-on Senior Technical Program Manager to help us deliver innovative Gen AI games that unlock new types of gameplay. In this role, you will work across a portfolio of AI games at different phases of the production lifecycle, acting as the connective tissue across GenAI tooling and capabilities, AWS, and studio teams. Your role will include helping onboard developers to our AI services with playbooks and clear guidelines on responsible AI usage, shaping our product roadmap, and assessing the partner landscape as we identify the best models and tools to power our use cases.

The ideal candidate has technical game development or adjacent experience and is passionate about the potential of GenAI in the creative space. They can operate in highly complex and ambiguous environments that involve cross-functional partnership, knowledge of industry trends, and comfort defining new products with new technology. This is an exciting opportunity to work in a dynamic environment where you’ll shape the future of gaming with AI.

Key job responsibilities
- Program Management: Lead end-to-end program management across a portfolio of Gen AI-powered game launches
- Stakeholder Coordination: Coordinate with game studios, external vendors, and cross-functional partners (Product, Engineering, Legal, Marketing, PR and more) ensuring alignment on project milestones, timelines, and deliverables.
- Technical Integration: Oversee technical integration of Gen AI features into game development pipelines as well as internal workflows ensuring compatibility and optimal performance.
- Developer Support: Help onboard developers to our AI tools and services by providing guidance and creating launch playbooks
- Launch Strategy: Develop and execute launch strategies, including marketing collaboration, user feedback loops, and post-launch support.
- Vendor Analysis and Management: Maintain a pulse on the AI tooling landscape and continually analyze best tools for our use cases and manage the vendor partnership process
- Performance Monitoring: Establish metrics and dashboards for performance and cost management

About the team
Amazon Luna is a cloud gaming service that makes it easy for anyone to play high-quality games on the devices they already own—including Fire TV and tablet devices, smartphones, tablets, web browsers, select smart televisions from LG and Samsung, and more--with no need to purchase new gaming consoles or equipment. Luna brings more than 50 games to Prime members at no additional cost, including GameNight, an evolving collection of more than 25 approachable, local multiplayer games designed to bring friends and family together in the living room. To play GameNight titles, simply scan a QR code on your TV and your smartphone becomes your controller. With simple and responsive touchscreen inputs and support for multiple local players, GameNight offers and easy and approachable way to play whether you’re an experienced gamer of a casual fan.

BASIC QUALIFICATIONS

- 7+ years of working directly with engineering teams experience
- 5+ years of technical product or program management experience
- 3+ years of software development experience
- 5+ years of technical program management working directly with software engineering teams experience
- Experience managing programs across cross functional teams, building processes and coordinating release schedules
- Experience in Games Industry (supporting title Development, Release, or Live Ops)

PREFERRED QUALIFICATIONS

- 5+ years of project management disciplines including scope, schedule, budget, quality, along with risk and critical path management experience
- Experience managing projects across cross functional teams, building sustainable processes and coordinating release schedules
- Experience defining KPI's/SLA's used to drive multi-million dollar businesses and reporting to senior leadership
- Strong technical fluency in Generative AI, including a deep understanding of large language models (LLMs), AI model behavior, and trade-offs (e.g., model size, latency, cost, and output quality).

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