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 solid 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.

Skills: storage,firmware,compute

📌 Head Of Ai Infrastructure — Cluster Deployment Compute & Storage Bengaluru
🏢 NAVA
📍 Bengaluru