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