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

Nava; Bengaluru; On-site; INR 4,500,000 - 6,500,000; Full time

Job summary

Nava is seeking an experienced Compute & Storage Deployment engineer to own GPU server commissioning, Linux provisioning, and cluster integration in a demanding AI infra environment.

You will validate topology, memory, thermals, and health, and you will automate provisioning and diagnostics while coordinating across GPU, storage, and networking dependencies.

Qualifications

• 10-15 years of Linux compute, storage, HPC or large-scale infrastructure experience.
• Hands-on NVIDIA GPU server deployment, BMC/Redfish, firmware and provisioning.
• Knowledge of PCIe, NVLink/NVSwitch, GPU drivers and diagnostic workflows.
• Experience with storage systems such as Lustre, BeeGFS, Ceph or IBM Storage Scale.
• Automation using Python/Bash/Ansible and strong debugging skills.

Responsibilities

• 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 tools.
• Deploy and tune distributed or parallel storage and validate capacity, throughput, latency and resilience.
• Own burn-in, benchmarks, NCCL/compute tests, defect closure and handover.
• Automate PXE/Redfish provisioning, drift controls and diagnostics.
• Lead engineers and OEM troubleshooting across GPU, server, storage, networking and platform dependencies.

Skills

Linux compute & HPC

NVIDIA GPU servers

Automation scripting

Cluster deployment

Hardware diagnostics

Tools

WEKA

Lustre

BeeGFS

IBM Storage Scale

Ceph

Job description

About 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 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.

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.

Skills: storage,firmware,compute