24 jobs found in Kansas

Own Every Moment at NetApp At NetApp, 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, NetApp is where your journey begins. 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). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details

Own Every Moment at NetApp At NetApp, 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, NetApp is where your journey begins. 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). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details

Own Every Moment at NetApp At NetApp, 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, NetApp is where your journey begins. 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). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details

Own Every Moment at NetApp At NetApp, 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, NetApp is where your journey begins. 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). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details

Own Every Moment at NetApp At NetApp, 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, NetApp is where your journey begins. 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). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details

Own Every Moment at NetApp At NetApp, 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, NetApp is where your journey begins. 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). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details

Own Every Moment at NetApp At NetApp, 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, NetApp is where your journey begins. 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). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details

Job Description Build your best future with the Johnson Controls team As a global leader in smart, healthy and sustainable buildings, our mission is to reimagine the performance of buildings to serve people, places and the planet. Join a winning team that enables you to build your best future! Our teams are uniquely positioned to support a multitude of industries across the globe. You will have the opportunity to develop yourself through meaningful work projects and learning opportunities. We strive to provide our employees with an experience, focused on supporting their physical, financial, and emotional wellbeing. Become a member of the Johnson Controls family and thrive in an empowering company culture where your voice and ideas will be heard - your next great opportunity is just a few clicks away! What we offer Competitive salary Paid vacation/holidays/sick time - 15 days of vacation first year. Comprehensive benefits package including 401K, medical, dental, and vision care. Extensive product and on the job/cross training opportunities. Encouraging and collaborative team environment Dedication to safety through our Zero Harm policy Check us Out: What will you do Under general direction, acts as the on-site project leader to plan, execute, and complete control systems projects with assigned customers. Performs or delegates tasks as required to execute and fully complete assigned projects including: hardware design, system programming, installation coordination, system and network commissioning and project closeout. Responsible for the overall financial results of assigned projects including: costs, project billings, and collections. Maintains an effective balance between customer satisfaction and project financial results. Actively pursues selling change orders. Coordinates communication with the customer during all phases of the project. Ensures proper execution of warranty. Provides work direction to subcontractors, electrical installers, technicians, designers, and administration as necessary. Ensures work performed is in compliance with state, local and Federal legal requirements and operates on the job with the highest of ethics. Adheres and ensures Johnson Controls staff and subcontractors adhere to all safety standards. How will you do it Completes typical installation hardware design and software programming using established standards as required. Provides detailed information to communicate design and operation to customers, Johnson Controls staff and subcontractors. Actively pursues additional work through change orders. Evaluates the contractual scope of work and the impact of client issued bulletins, field directives and/or scheduling changes. Communicates both technical and business-related issues with the clients. Manages the selection, ordering, budget and delivery schedule of materials to be procured for the projects assigned. Secures pricing and availability from outside vendors and suppliers, mechanical and electrical subcontractors including scope, terms and conditions. May request several bids in pricing process. Oversees subcontractor payment and billing processes. Provides jobsite coordination for panel and field device physical locations. Ensures installation is in accordance with Johnson Controls literature and project requirements. Manages and completes the loading, device verification, and commissioning of all system controllers as required. Validates complete system functionality and resolves issues with subcontractors and others to ensure proper operation. Provides accurate project as-built and commissioning documentation. Manages costs, billings and collections. Completes project billings in a timely and accurate format to the client. Maintains profitability goals and positive cash flow. Schedules monthly project cost in cost accounting system. Responsible for project cost hitting projects as scheduled. Provides coaching, mentoring and technical assistance to System Technicians, Electrical Installers and System Designers. Provides technical assistance to subcontractors. Ensures that delegated tasks are done accurately, on time, billed, within budget and within scope of the contract. Effectively communicates the status of projects to management as required and provides monthly forecasts of revenue, costs, and gross margin. Sends warranty letter to JCI customer upon substantial completion of project and ensures proper execution of warranty. Develops project plans and coordinates the required resources to ensure timely and cost-effective installation and completion of assigned projects. Performs site-specific training for owner/operator on the total system in full or with assistance. Ensures owner/operator has been trained. Provides proper documentation and manuals for system operation. Develops and maintains viable long-term relationships with contractors, clients, consultants and subcontractors. Attends job progress meetings as required. Adheres to safety standards. High degree of regard to employee and subcontractor safety. What we look for Required Minimum of a technical Associates Degree with two years of field experience or four years of equivalent related field experience in the HVAC industry. Must have relevant technical hands-on experience in systems design and commissioning of digital HVAC controls systems. Must have the ability to communicate technical material to a non-technical audience. Must demonstrate the ability to perform work independently. Preferred Strong personal computer skills. Must have knowledge of control theory and HVAC systems. Knowledge of project accounting, costing principals and contracting preferred. HIRING SALARY RANGE: $76,100-$104,600 (Salary to be determined by the education, experience, knowledge, skills, and abilities of the applicant, internal equity, and alignment with market data.) This role offers a competitive Bonus plan that will take into account individual, group, and corporate performance. This position includes a competitive benefits package. For details, please visit the About Us tab on the Johnson Controls Careers site at -us

Own Every Moment at NetApp At NetApp, 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, NetApp is where your journey begins. 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). Compensation: The target salary range for this position is $228,000 - $325,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU's), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process. Equal Opportunity Employer: NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal . click apply for full job details