Principal Product Manager

Location: Goddard

Own Every Moment at Net App At Net App, your ideas power innovation. We lead in intelligent data infrastructure-delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud. Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow.

If you're ready to bring curiosity, creativity, and drive to every moment, Net App is where your journey begins.

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

Role Overview We need a highly strategic and deeply technical principal PM who can:
Define 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 Net App differentiation (enterprise data services, multiprotocol access, lifecycle management, resiliency, and cross-cloud consistency where relevant).

Responsibilities 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 file system 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 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 realities.

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…