Senior Solutions Architect, GPU System

We are seeking a Senior Solutions Architect to join our Solutions Architect team. This role involves leading presales and architecture engagements with AI industry customers, focusing on GPU servers, AI clusters, and large-scale training/inference platforms built on NVIDIA HGX, GPU systems, and reference architectures.

Key responsibilities include:

• Design and validate end-to-end AI data center solutions, including server platforms, storage connectivity, and high-performance networking based on Spectrum, Quantum, ConnectX, and BlueField.

• Define system architectures for AI supercomputing, LLM training, and inference workloads, including node configuration, GPU topology, PCIe/NVLink considerations, and network design.

• Support business teams in exploring, developing, and deploying NVIDIA server and GPU solution opportunities, from early technical discovery through POC and production rollout.

• Own and execute POCs and hands-on labs that validate GPU server performance, scalability, reliability, and interoperability across compute, storage, and network domains.

• Troubleshoot complex end-to-end issues involving GPU servers, firmware, drivers, operating systems, and networking stacks, and drive fixes with internal R&D and partners.

• Provide structured feedback on platform features, system requirements, and customer needs to server OEMs, engineering, and product teams to improve NVIDIA AI platforms and ecosystems.

Requirements include:

• BS/BA in Computer Science, Electrical/Computer Engineering, or equivalent experience, with 6+ years of experience with data center servers, GPU platforms, or large-scale AI/HPC infrastructure.

• Strong understanding of GPU server architecture: CPU/GPU balance, memory and PCIe/NVLink topology, storage and NIC placement, and power/cooling considerations.

• Proven experience designing or operating AI or HPC clusters using GPU-accelerated servers in cloud or on-prem data centers.

• Solid background in data center and cloud networking for AI workloads, including leaf-spine fabrics, RDMA and high-bandwidth/low-latency designs.

• Strong Linux system and Linux networking skills, including driver, firmware, and OS-level tuning for GPU and NIC performance.

• Knowledge and experience with K8S, RDMA/RoCE and, ideally, RoCE and Infiniband AI clusters.

• Excellent communication skills to collaborate with customers, server OEMs, and internal architecture and engineering teams.