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Job Summary

Role Overview

Responsibilities

AI strategy & roadmap 

• Own end-to-endAI strategyfor ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. 
• Prioritize investments acrossperformance,scale,data services,protocol and API surfaces, andoperational excellencefor 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 withMicrosoftteams acrossAzure 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 withAzure-wide AI dataguidance and reference architectures, and feedreal customer workload evidenceback into joint planning. 

Cross-functional leadership 

• Lead acrossengineering, product marketing, sales, customer success, and professional servicesto ship capabilities and repeatablereference architectures / proof points. 
• Engagestrategic customersanddesign partnersto 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 intodifferentiated bets. 
• Represent ANF as acredible technical executivein 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, andHPC/simulation-including compliance and data residency realities. 

AI strategy & roadmap 

• Own end-to-endAI strategyfor ANF: problem selection, success metrics, phased delivery, and competitive positioning vs. other Azure and AI-native storage options. 
• Prioritize investments acrossperformance,scale,data services,protocol and API surfaces, andoperational excellencefor 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 withMicrosoftteams acrossAzure 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 withAzure-wide AI dataguidance and reference architectures, and feedreal customer workload evidenceback into joint planning. 

Cross-functional leadership 

• Lead acrossengineering, product marketing, sales, customer success, and professional servicesto ship capabilities and repeatablereference architectures / proof points. 
• Engagestrategic customersanddesign partnersto 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 intodifferentiated bets. 
• Represent ANF as acredible technical executivein 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, andHPC/simulation-including compliance and data residency realities. 

Job Requirements

Required 

• 10+ yearsproduct management incloud infrastructure,enterprise storage,AI/ML infrastructure, ordata platforms(principal scope: portfolio strategy, multi-team alignment, executive storytelling). 
• Strong command ofenterprise storage: NFS/SMB semantics, snapshots/clones, replication, backup integration patterns, capacity/performance tiers, and large-scale filesystem behavior under parallel workloads. 
• Hands-on familiarity withmodern AI stacks: LLMs, RAG architectures, embeddings/vector retrieval patterns, training vs. inference IO profiles, orchestration, and enterprise AI data pipelines. 
• Demonstrated success influencingengineering and partner roadmapswithout direct authority; experience withhyperscaler first-partyor deeply partnered services is a strong plus. 
• Excellent written and verbal communication tocustomers, executives, and engineers. 

Preferred 

• Direct experience withMicrosoft AzureAI services,GPUestates on Azure, and/orAzure Kubernetes Service+ ML platform integrations. 
• Familiarity withDatabricks,Iceberg/Delta-class open table patterns,Kubernetesstorage patterns,NVIDIA AIsoftware stacks, andenterprise MLOpsrelease cadences. 
• Background inregulated industriesand enterprise security/governance requirements for AI data. 

Education

• MBAor advanced degree inCS/Engineering(helpful, not a substitute for demonstrated technical depth).