Skills

- Knowledge &
- Posting Location: GPGPU, CUDA, Artificial Intelligence, HPC

Minimum Qualification

- Bachelor of Technology (BTech)

Build and operate large-scale GPU compute pods to deliver predictable, high-throughput, low-latency training and inference services across 10K GPU cluster.

Roles &

- ResponsibilitiesImplementation

- Stand up multi-pod GPU clusters (rack/power/cooling layouts
- TOR/leaf connectivity
- IB/Ethernet host configs).

- Implement GPU partitioning (MIG/vGPU profiles) and quota policies for multi-tenant environments.

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

Operations

- Own day-2 operations across firmware/driver/DCGM/NVML lifecycles; execute change windows with zero/low downtime.

- Capacity planning (GPU/CPU/Memory/NIC) and bin-packing 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, HPL-AI, MLPerf-like internal suites); track performance regressions with SLOs.

Reliability &

- Incident

- Lead P0/P1 incident response for GPU, CUDA, or scheduler issues; perform root-cause 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 auto-healing 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 Qualified (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/B-series 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

5. GPUDirect Storage

6. Prometheus/Grafana

7. ELK/Splunk

Locations

- GPU Infrastructure Engineer / HPC Engine

- LNT/GIE-HE/1920234 IU72-Larsen &
- Toubro - VyomaAMN Tower, Powai

- Posted On: 10 Nov 2026

- End Date: 09 May 2027

- Required Experience: 6 - 10 Years

📌 GPU Infrastructure Engineer / HPC Engine... (India)
🏢 Larsen & Toubro Infotech
📍 India