Principal Product Manager

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…