Head of AI Infrastructure — Cluster Deployment (Compute & Storage)

About Nava

Nava is a neocloud company purpose-built for the AI era. We design, deploy, and operate large-scale GPU infrastructure and deliver inference-as-a-service to teams building the next generation of AI products. Our platform runs on NVIDIA GPU systems, high-performance RDMA fabrics, and a fully automated, software-defined operations model. Every engineer at Nava works close to the metal, on infrastructure built to keep GPUs saturated and models serving.

About the Role

The Compute & Storage Deployment team owns commissioning of NVIDIA GPU servers, supporting CPU infrastructure and high-performance storage, from installed hardware through benchmarked, production-ready capacity.

Lead deployment of NVIDIA GPU servers and high-performance storage systems. Own hardware commissioning, firmware configuration, automated provisioning, GPU diagnostics, storage integration, benchmarking, and production acceptance across distributed clusters.

What You’ll Do

• Lead server installation, BMC/BIOS/firmware baselines, Linux provisioning, NVIDIA drivers and cluster integration.

• Validate GPU topology, PCIe, NVLink/NVSwitch, memory, thermals and hardware health with DCGM and other tools.

• Deploy and tune distributed or parallel storage and validate capacity, throughput, latency and resilience.

• Own burn-in, acceptance benchmarks, NCCL/compute tests, defect closure and documented handover.

• Automate PXE/Redfish-based provisioning, configuration drift controls and repeatable diagnostics.

• Lead engineers and OEM troubleshooting across GPU, server, storage, networking and platform dependencies.

What You’ll Need

• 10–15 years in Linux compute, storage, HPC or large-scale infrastructure deployment.

• Hands-on NVIDIA GPU server and fleet commissioning, BMC/Redfish, firmware and provisioning.

• Practical knowledge of PCIe, NVLink/NVSwitch, GPU drivers and diagnostic workflows.

• Experience with WEKA, Lustre, BeeGFS, IBM Storage Scale, Ceph or similar storage.

• Python/Bash/Ansible automation and strong systems-level debugging.

• Within that, hands-on GPU / AI-datacenter exposure is what we weigh most — we value demonstrated depth over years, as the field is still emerging.

• DGX/HGX or rack-scale NVIDIA systems, Slurm/Kubernetes, cluster-level benchmarking.

• Experience with large-scale hardware infrastructure providers.