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Tier-1 brand, metro location, and pan-India hybrid role increase applicant density despite niche GPU specialization.
Highly specialized GPU/cloud/HPC skillset limits cross-industry transferability.
Explicit 11–15 years plus mandatory NVIDIA GPU, cloud, Kubernetes, Terraform, and HPC expertise.
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Design and engineer GPU-focused hybrid cloud infrastructure, including GPU landing zones, clusters, and high-throughput storage tailored for distributed AI workloads.
Select and optimize NVIDIA GPU instances and cluster patterns for varied AI tasks such as training, fine-tuning, and inference.
Lead cloud GPU cluster engineering using managed Kubernetes, HPC schedulers with advanced GPU scheduling, high-performance networking, and observability.
11-15 years of relevant experience in infrastructure, cloud, SRE, HPC, or platform engineering.
Strong hands-on expertise in NVIDIA GPU infrastructure, including CUDA, NCCL, GPU drivers, and production AI/GPU platforms.
Proficiency in at least one hyperscale cloud platform (AWS, Azure, or Google Cloud) and Kubernetes/OpenShift and/or Slurm.
Bachelor’s or Master’s degree in Computer Science, IT, Engineering, or related technical field.
Experienced in designing and operating complex AI GPU cluster architectures spanning multi-cloud and hybrid environments.
Skilled in infrastructure automation and GitOps workflows leveraging Terraform, CI/CD pipelines, and container orchestration.
Comfortable managing distributed AI workloads requiring expertise in GPU scheduling, high-performance networking, storage, and hybrid connectivity setups.