Job Purpose

Build and operate largescale GPU compute pods to deliver predictable, highthroughput, lowlatency training and inference services across 10K GPU cluster.

Roles & Responsibilities

Implementation

Stand up multipod GPU clusters (rack/power/cooling layouts; TOR/leaf connectivity; IB/Ethernet host configs).

Implement GPU partitioning (MIG/vGPU profiles) and quota policies for multitenant environments.

Integrate cluster schedulers (Slurm/Kubernetes) with GPU device plugins, node feature discovery, and accounting/quotas.

Operations

Own day2 operations across firmware/driver/DCGM/NVML lifecycles; execute change windows with zero/low downtime.

Capacity planning (GPU/CPU/Memory/NIC) and binpacking strategies for heterogeneous GPU SKUs.

Performance & Optimization

Tune NCCL/UCX, GPU clocks/persistence, GPU Direct Storage, NUMA/locality, and CUDA runtime parameters.

Drive benchmarking and acceptance (HPL, HPLAI, MLPerflike internal suites); track perf regressions with SLOs.

Reliability & Incident

Lead P0/P1 incident response for GPU, CUDA, or scheduler issues; perform rootcause and preventative actions.

Act as the primary technical lead during production outages, coordinating cross-functional teams across Networking, GPU Operations, Platform Engineering, Storage, and Application teams to restore services within SLA targets.

Perform detailed Root Cause Analysis (RCA) for network, GPU, CUDA, NCCL, RDMA, and scheduler-related failures, identifying underlying causes and implementing preventive and corrective actions.

Collaborate with platform and GPU engineering teams to resolve issues impacting CUDA jobs, Kubernetes scheduling, Slurm workload management, GPU resource allocation, and large-scale AI training environments.

Define golden images; implement node remediation (cordon/drain/reimage) and autohealing workflows.

Security & Compliance

Enforce GPU tenancy isolation (MIG, cgroup/device cgroup, mpsd), secure drivers/containers, SBOM and image scanning.

Documentation & Enablement

Publish runbooks, performance baselines, and application tuning guides for LLM training and inference.

Experience & Educational Requirement

BE/B-Tech or equivalent with Computer Science or Electronics & Communication

NVIDIA Certified Professional (AI Infrastructure); Linux (RHCE/LPIC) preferred.

RELEVANT EXPERIENCE

7–12 years building/operating HPC/AI GPU clusters at scale; deep CUDA/MIG/vGPU expertise.
Proven with H100/Bseries class GPUs, Slurm and/or Kubernetes device scheduling; NCCL/UCX performance tuning.

Tools / Tech

1. CUDA, NCCL, cuDNN, TensorRT; 2. DCGM/NVML, nvidia-smi; 3. Slurm, K8s (device plugin, DaemonSets, NFD); 4. Helm/ArgoCD; 6. GPUDirect Storage; 7. Prometheus/Grafana; 8. ELK/Splunk.

GPU Infrastructure Engineer / HPC Engineer