Product Manager 6
Job Summary
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.
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 NetApp 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 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 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 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 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.
Job Requirements
Required
- 10+ years product management 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 IO 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 to 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.
Education
- MBA or advanced degree in CS/Engineering (helpful, not a substitute for demonstrated technical depth).
All internal movements within the Product Group via requisition will be lateral, offering valuable growth opportunities to extend your skills in a new area. Opportunities for a promotion will be reviewed in the normal course of business, aligned with our promotion process.
Equal Opportunity Employer:
NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal, state and local laws that prohibit employment discrimination based on age, race, color, gender, sexual orientation, gender identity, national origin, religion, disability or genetic information, pregnancy, protected veteran status, and any other protected classification.
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