Senior Solutions Architect - AI, HPC, & Lustre

NetApp’s Solutions Architecture (SA) team partners with Enterprise Sales to drive advanced AI, analytics, and high-performance computing (HPC) conversations with customers, stepping in when deep technical expertise is required beyond day-to-day SE support. As the Senior Solutions Architect for AI, Analytics & Lustre-based HPC Storage, you will support Enterprise customers across the United States, helping them design and scale AI/ML and data-intensive workloads that depend on ultra-high-performance storage architectures - whether deployed on-premises or across NeoCloud and AI hyperscaler GPU environments. 

This role is uniquely focused on NetApp’s high-performance storage solutions for Lustre, a parallel file system widely used in HPC and AI environments to deliver extreme throughput, low latency, and scalable access for GPU clusters and large-scale data processing. You will help customers understand how their data infrastructure, particularly workloads requiring massive bandwidth (e.g., model training, simulations, analytics pipelines), can evolve using NetApp’s Lustre-enabled solutions to eliminate storage bottlenecks and maximize GPU utilization. As customers increasingly leverage NeoCloud and AI hyperscaler providers for GPU-dense compute, you will guide them in architecting storage solutions that span on-prem infrastructure and these cloud-based GPU environments to meet their HPC and AI training demands. 

Customer Engagement & AI / HPC Strategy 

• Lead advanced discussions with Enterprise customers around AI, analytics, and high-performance computing (HPC) architectures.
• Assess customer environments, focusing on data-intensive workloads, GPU clusters, and performance bottlenecks in storage pipelines.
• Identify opportunities where parallel file systems like Lustre can accelerate AI training, simulation workloads, and large-scale analytics.
• Present architectures spanning GenAI, distributed training pipelines, high-throughput data ingestion, and HPC workflows.
• Help customers architect and optimize HPC and AI storage solutions that bridge on-premises infrastructure with NeoCloud and AI hyperscaler GPU providers (e.g., CoreWeave, Lambda, Crusoe), ensuring high-performance data access regardless of where compute resides. 

Field Partnership & Enablement 

• Partner with Client Executives and SEs to align AI + HPC + storage strategy with account plans.
• Enable the field to identify and position opportunities involving:
• GPU saturation challenges
• AI training data pipelines
• HPC simulation and research workloads
• Deliver workshops and technical sessions on AI infrastructure design, GPU/storage balance, and Lustre-based architectures. 

Technical Leadership (Lustre + AI Infrastructure Focus) 

• Architect high-performance AI and HPC storage solutions leveraging NetApp E-Series and Lustre.
• Provide Design Guidance 
• Parallel file systems (Lustre architecture, metadata + object storage separation)
• High-throughput networking (InfiniBand, RDMA, high-speed Ethernet)
• GPU cluster integration (NVIDIA DGX, CUDA ecosystems)
• Data pipelines optimized for large-scale distributed training
• Storage connectivity and data movement strategies for NeoCloud and AI hyperscaler GPU environments
• Design systems that scale to petabyte-level datasets and extreme throughput requirements typical of AI and HPC.
• Support demos, benchmarks, POCs, and pilot environments demonstrating performance at scale and GPU efficiency gains. 

Pipeline Development & Cross-Functional Collaboration

• Drive pipeline growth focused on AI infrastructure modernization and HPC storage transformation.
• Contribute to RFP/RFI responses emphasizing performance, scalability, and architecture differentiation.
• Share insights with product teams to influence NetApp’s evolving AI and HPC storage portfolio. 

Stay Current on Technologies 

• Parallel file systems (Lustre, BeeGFS)
• GPU ecosystems (NVIDIA, CUDA, DGX SuperPOD)
• AI/ML frameworks (PyTorch, TensorFlow)
• HPC and AI infrastructure trends
• NeoCloud and AI hyperscaler GPU ecosystems (e.g., CoreWeave, Lambda, Crusoe) and their evolving storage requirements