Principal Technical Product Manager

About the role

What you'll do

• AI Runtime, Agentic Platform & Compute, you will own the product strategy for this critical layer of AI Factory. You will transform GPU compute, Kubernetes, model serving, inference, agent runtimes, agent orchestration, multi-agent execution, lifecycle management, and observability into enterprise services that development teams can easily consume.
• You will define the product vision, roadmap, service model, and investment priorities while partnering with Engineering, Compute, Cloud, Security, Governance, Risk, Compliance, and Architecture leaders to build a high-performance enterprise AI platform.
• This role owns the AI runtime, compute, and operational agent platform experience. It does not own underlying physical infrastructure, enterprise model approval and Responsible AI policy processes, or the business-specific agents, AI applications, and solutions built by consuming teams.
• AI can only scale across the enterprise when engineering teams have secure, reliable, and easy-to-consume runtime and agent platform capabilities beneath their solutions.
• You will turn advanced GPU, Kubernetes, AI runtime, and agent execution technologies into standardized enterprise services that help teams move AI workloads and agents into production faster, while integrating with enterprise-approved model access, security, and governance services.
• This is an opportunity to shape a foundational AI platform serving one of the world's largest healthcare enterprises and influence how engineering teams securely deploy and operate AI workloads and agents at enterprise scale.
• Primary
• Own the product vision, strategy, roadmap, and investment priorities for AI Factory Runtime and Agentic Platform Services across private cloud, public cloud, and hybrid environments
• Define clear product boundaries, ownership, and integration points across AI Factory, Compute, Cloud, Security, Governance, Architecture, enterprise AI enablement services, and consuming AI solution teams
• Productize GPU compute, Kubernetes/OpenShift, model serving, LLM inference, agent runtimes, agent orchestration, multi-agent execution, agent lifecycle management, and platform APIs into standardized services engineering teams can easily consume
• Define the roadmap and service model for operational agent platform capabilities, including agent deployment, execution, orchestration, scaling, lifecycle management, observability, tool connectivity, and governance integration
• Create a leading developer experience, including self-service onboarding, service tiers, APIs, SLOs/SLAs, documentation, consumption models, and platform automation

What you'll bring

• 10+ years of experience across technical product management, platform engineering, cloud infrastructure, architecture, or related technology leadership roles, including significant product ownership or product leadership experience
• Experience owning, designing, or shaping an enterprise platform, developer platform, cloud service, Kubernetes platform, distributed compute platform, agent platform, or comparable technology product
• Experience defining product strategy, roadmaps, service models, SLOs/SLAs, adoption metrics, consumption models, and investment priorities
• Solid understanding of Kubernetes/container platforms, cloud infrastructure, distributed systems, platform engineering, or GPU/AI compute architectures
• Demonstrated ability to translate complex infrastructure, runtime, and agent platform capabilities into simple, consumable platform products and developer experiences.
• Demonstrated ability to communicate complex platform strategies to executive audiences and influence senior engineering, architecture, security, product, and business leaders without direct authority
• Proven ability to build consensus and make trade-offs across competing customer, engineering, architecture, security, operational, and business priorities
• Advanced degree in Computer Science, Engineering, Data Science, Business, or a related discipline
• Experience with AI/ML infrastructure, GPU compute, model serving, LLM inference, agent runtimes, agentic platforms, agent orchestration frameworks, multi-agent systems, or AI runtime platforms
• Experience with Kubernetes, Red Hat OpenShift, NVIDIA GPU technologies, or other large-scale container and accelerated-compute environments
• Experience with enterprise AI or cloud AI platforms such as Azure AI Foundry, AWS Bedrock, Google Vertex AI, Databricks, or similar technologies
• Experience w