{"id":4012174,"url":"https://alion.io/job/larsen-toubro-gpu-infrastructure-engineer-hpc-engineer","title":"GPU Infrastructure Engineer / HPC Engineer","company":{"id":2124,"name":"Larsen & Toubro","domain":"larsentoubro.com","url":"https://alion.io/company/larsentoubro","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"A","score":90,"open_postings":12,"ghost_share":0,"stale_share":0.333,"repost_share":0.083,"time_to_fill_p50_days":41,"computed_at":"2026-11-11T05:49:22Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ArgoCD","optional":false},{"name":"CUDA","optional":false},{"name":"cuDNN","optional":false},{"name":"Grafana","optional":false},{"name":"Helm","optional":false},{"name":"HPC","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"NCCL","optional":false},{"name":"Prometheus","optional":false},{"name":"SBOM","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"SLURM","optional":false},{"name":"Splunk","optional":false},{"name":"TensorRT","optional":false}],"status":"live","first_seen_at":"2026-11-10T10:42:21Z","employer_posted_date":"2026-11-10","last_verified_at":"2026-11-11T07:11:58Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Build and operate large?scale GPU compute pods to deliver predictable, high?throughput, low?latency training and inference services across 10K GPU cluster.\nRoles & Responsibilities\n Implementation\nStand up multi?pod GPU clusters (rack/power/cooling layouts; TOR/leaf connectivity; IB/Ethernet host configs).\nImplement GPU partitioning (MIG/vGPU profiles) and quota policies for multi?tenant environments.\nIntegrate cluster schedulers (Slurm/Kubernetes) with GPU device plugins, node feature discovery, and accounting/quotas.\n Operations\nOwn day?2 operations across firmware/driver/DCGM/NVML lifecycles; execute change windows with zero/low downtime.\nCapacity planning (GPU/CPU/Memory/NIC) and bin?packing strategies for heterogeneous GPU SKUs.\n Performance & Optimization\nTune NCCL/UCX, GPU clocks/persistence, GPU Direct Storage, NUMA/locality, and CUDA runtime parameters.\nDrive benchmarking and acceptance (HPL, HPL?AI, MLPerf?like internal suites); track perf regressions with SLOs.\n Reliability & Incident\nLead P0/P1 incident response for GPU, CUDA, or scheduler issues; perform root?cause and preventative actions.\nAct 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.\nPerform detailed Root Cause Analysis (RCA) for network, GPU, CUDA, NCCL, RDMA, and scheduler-related failures, identifying underlying causes and implementing preventive and corrective actions.\nCollaborate 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.\nDefine golden images; implement node remediation (cordon/drain/reimage) and auto?healing workflows.\n Security & Compliance\nEnforce GPU tenancy isolation (MIG, cgroup/device cgroup, mpsd), secure drivers/containers, SBOM and image scanning.\nDocumentation & Enablement\nPublish runbooks, performance baselines, and application tuning guides for LLM training and inference.\nExperience & Educational Requirement\nBE/B-Tech or equivalent with Computer Science or Electronics & Communication\nNVIDIA Certified Professional (AI Infrastructure); Linux (RHCE/LPIC) preferred.\nRELEVANT EXPERIENCE\n7-12 years building/operating HPC/AI GPU clusters at scale; deep CUDA/MIG/vGPU expertise.\nProven with H100/B?series class GPUs, Slurm and/or Kubernetes device scheduling; NCCL/UCX performance tuning.\nTools / Tech\n1. 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. 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