Principal Product Manager

HPE Private Cloud AI is the turnkey, NVIDIA co-engineered private AI cloud that lets enterprises run inference, RAG, fine-tuning, and agentic AI on their own infrastructure, delivered as a cloud experience through HPE GreenLake. We are hiring a Principal Product Manager to own a major slice of the platform end to end, from GPU compute and AI fabric to the AI software stack developers use every day. You will set strategy, win the hard trade-offs, and ship the capabilities that make Private Cloud AI the obvious choice over public cloud and neocloud alternatives.

• →Own strategy and roadmap for your Private Cloud AI area across GPU compute, AI networking, and the AI software stack, tied to clear revenue, adoption, and utilization goals.
• →Define the platform across layers: GPU and accelerator configurations, Spectrum-X and InfiniBand fabrics, storage, Kubernetes orchestration, scheduling, and the curated AI tools customers deploy on day one.
• →Make GPUs pay off: drive the features that raise utilization and time-to-value, such as GPU sharing, fair-share quotas, gang scheduling, and multi-tenant isolation.
• →Lead the agentic AI roadmap: bring secure agent runtimes, model serving, and developer frameworks into the platform as supported, enterprise-ready experiences.
• →Partner deeply with NVIDIA and the open-source ecosystem to co-define integrations, validated designs, and release timing.
• →Be the voice of the customer: work directly with enterprise, sovereign, and service-provider buyers to turn their workloads into crisp requirements and PRDs.
• →Drive launches end to end with engineering, solutions, marketing, and sales, and track results with data after GA.

• 10+ years of product management, engineering, or architecture experience, including 5+ years owning AI/ML, HPC, or cloud infrastructure products.
• Hands-on depth in AI compute infrastructure: NVIDIA GPU platforms (e.g., H100/H200, Blackwell), server and rack architecture, power and cooling trade-offs, and sizing for training, fine-tuning, and inference.
• Hands-on depth in AI networking: RDMA, RoCE and InfiniBand, Ethernet AI fabrics (e.g., NVIDIA Spectrum-X), DPUs/SuperNICs, and how fabric design affects job performance.
• Working knowledge of Kubernetes and cloud-native operations, and of how GPU workloads are scheduled and isolated in multi-tenant clusters.
• Track record of shipping complex hardware-plus-software solutions to enterprise customers, from requirements through launch and adoption.
• Strong written and verbal communication; credible with engineers, executives, and customers alike.
• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience.

• AI frameworks and MLOps: LangChain, LangFlow, MLflow, Ray, and SGLang, plus related serving engines such as vLLM.
• GPU scheduling: NVIDIA KAI Scheduler or similar Kubernetes-native schedulers (Run:ai, Kueue, Volcano, Slurm).
• Agent runtimes and harnesses: NVIDIA OpenShell and NemoClaw, OpenClaw, and Hermes, including sandboxing, policy, and inference routing for always-on agents.
• NVIDIA AI Enterprise: NIM microservices, NeMo, Triton/Dynamo, the GPU and Network Operators, and NVIDIA AI Blueprints.
• Experience with private, sovereign, or regulated-industry AI deployments, or with hybrid cloud platforms such as HPE GreenLake.
• Prior engineering, solutions architecture, or forward-deployed experience; MBA or advanced technical degree a plus.

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