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Job Purpose Build and operate large scale GPU compute pods to deliver predictable, high throughput, low latency training and inference services across 10K GPU cluster. Roles & Responsibilities - Implementation 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 perf 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 - 712 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 - 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. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.