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).