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NetApp is hiring a principal-level product leader to own the AI product strategy for Azure NetApp Files (ANF)-a first-party, fully managed enterprise file service on Microsoft Azure, delivered in deep partnership between NetApp and Microsoft.

In the spirit of NetApp's "business builder" cloud roles, you will translate a fast-moving AI landscape into differentiated platform capabilities, joint roadmap bets with Microsoft, and enterprise outcomes (performance, data locality, governance, and time-to-value for AI pipelines).

You will sit at the intersection of enterprise storage, Azure AI infrastructure, and industry AI workloads, ensuring ANF is positioned and built as a strategic data foundation for training, inference, RAG, analytics, simulation, and agentic workflows-without forcing customers to abandon enterprise file semantics, protection, or hybrid operating models.

• We need a highly strategic and deeply technical principal PM who can:

  • Define a multi-year AI vision and roadmap for ANF in the context of Azure AI services, GPU estates, data platforms, and regulated enterprise environments.

  • Turn emerging patterns (LLMs, RAG, agents, orchestration, multimodal data, vector retrieval, high-throughput checkpointing) into concrete product requirements and joint go-to-market narratives with Microsoft.

  • Balance hyperscaler co-development constraints with NetApp differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency, where relevant).

AI strategy & Roadmap

• Own end-to-end AI strategy for ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options.
• Prioritize investments across performance, scale, data services, protocol and API surfaces, and operational excellence for AI pipelines.

Workload-led product definition
Drive requirements for AI-centric scenarios, including:

• Training and inference data planes (high throughput, low latency, checkpointing, bursty I/O)
• RAG and enterprise search (datasets, versioning, clones, refresh patterns)
• Agentic workflows and orchestration (durable shared state, tool/data access patterns-where productized responsibly)
• Large multimodal and enterprise datasets (governance, access control, lifecycle)
• Analytics and simulation adjacencies (HPC/EDA-style throughput, shared filesystem semantics)

Hyperscaler & Ecosystem Partnership

• Partner with Microsoft teams across Azure AI / Foundry, Azure Machine Learning, AKS/container platforms, GPU infrastructure, data/analytics (e.g., Databricks-style patterns on Azure), and core Azure storage/networking dependencies.
• Align ANF's AI story with Azure-wide AI data guidance and reference architectures, and feed real customer workload evidence back into joint planning.

Cross-functional leadership

• Lead across engineering, product marketing, sales, customer success, and professional services to ship capabilities and repeatable reference architectures / proof points.
• Engage strategic customers and design partners to validate pain, quantify value, and de-risk roadmap bets.

Market intelligence & Evangelism

• Monitor AI infrastructure trends (models, frameworks, orchestration, data formats) and competitor moves; translate these into differentiated bets.
• Represent ANF as a credible technical executive in briefings, advisory councils, and industry forums.

Industry Segmentation

• Tailor AI storage strategy for segments where file semantics and performance matter, for example: semiconductor/EDA, manufacturing, healthcare imaging, financial services, energy, media & entertainment, and HPC/simulation-including compliance and data residency requirements.

• 10+ years of product management experience in cloud infrastructure, enterprise storage, AI/ML infrastructure, or data platforms (principal scope: portfolio strategy, multi-team alignment, executive storytelling).
• Strong command of enterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads.
• Hands-on familiarity with modern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference I/O profiles, orchestration, and enterprise AI data pipelines.
• Demonstrated success influencing engineering and partner roadmaps without direct authority; experience with hyperscaler first-party or deeply partnered services is a strong plus.
• Excellent written and verbal communication skills for customers, executives, and engineers.

Preferred

• Direct experience with Microsoft Azure AI services, GPU estates on Azure, and/or Azure Kubernetes Service + ML platform integrations.
• Familiarity with Databricks, Iceberg/Delta-class open table patterns, Kubernetes storage patterns, NVIDIA AI software stacks, and enterprise MLOps release cadences.
• Background in regulated industries and enterprise security/governance requirements for AI data.

• MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth).