Senior Engineer, Compute

Team: MS Infrastructure

Location: Gurugram, Haryana, Bangalore, Hyderabad

Commitment: Full Time

Workplace Type: hybrid

Key Responsibilities

• Provide enterprise-level operational support to Managed Services customers for incident, problem, and change management activities 

• Manage and maintain large-scale HPC and GPU compute clusters for AI training, inference, simulation, and data-intensive workloads 

• Deploy and administer Linux-based compute environments, including operating systems, drivers, firmware, CUDA or equivalent accelerator software, libraries, and runtime dependencies 

• Operate workload scheduling and orchestration platforms such as Slurm, Kubernetes, Ray, or comparable technologies 

• Benchmark and tune end-to-end workload performance 

• Troubleshoot complex failures from bare metal and firmware through the operating system, drivers, middleware, distributed frameworks, and user applications in step with other teams 

• Build automation for provisioning, configuration management, patching, validation, monitoring, and lifecycle management using Python, Bash, Ansible, Terraform, or similar tools 

• Develop operational dashboards, alerts, health checks, and capacity-planning processes that improve availability and resource utilization 

• Establish repeatable processes for incident response, root-cause analysis, change management, and operational readiness 

• Create technical documentation, runbooks, architecture diagrams, and user guidance for HPC and AI platforms 

• Participate in on-call rotation 

• Completion of training and certification as assigned to further skills and knowledge

Required Qualifications

• • Bachelor’s degree in computer science, computer engineering, electrical engineering, or a related technical field; equivalent practical experience may be considered 

  • Four or more years of experience deploying, operating, or engineering HPC, GPU, distributed systems, or cloud infrastructure 

  • Strong Linux or UNIX systems administration experience in production environments 

  • Hands-on experience with GPU computing, accelerator infrastructure, or large-scale AI workloads 

  • Experience with at least one workload manager or orchestrator, such as Slurm, Kubernetes, Run:ai, or PBS 

  • Strong scripting and automation skills in Python, Bash, or a comparable language 

  • Working knowledge of networking fundamentals, including TCP/IP, DNS, DHCP, routing, firewalls, Ethernet, and high-speed interconnects 

  • Experience diagnosing performance, reliability, and capacity issues across multiple infrastructure layers in a physical datacenter environment 

  • Ability to document systems clearly and communicate effectively with both technical and nontechnical stakeholders 

  • Demonstrated ability to work independently, prioritize operational risk, and collaborate in a fast-moving environment  

Preferred Qualifications

• Experience with NVIDIA GPU platforms, CUDA, NCCL, DGX, HGX, or comparable accelerator technologies 

• Experience with InfiniBand, RoCE, RDMA, congestion control, or lossless network fabrics 

• Experience with Slurm configuration, fair-share scheduling, QoS, accounting, job arrays, and high-throughput workload management 

• Experience with Kubernetes, GPU operators, container runtimes, Helm, or cloud-native infrastructure 

• Experience with parallel and distributed storage platforms such as Lustre, GPFS, BeeGFS, Ceph, ZFS, or XFS 

• Familiarity with PyTorch, TensorFlow, MPI, OpenMP, UCX, NCCL, Ray, or distributed model-training frameworks 

• Experience with observability platforms, telemetry pipelines, and performance-profiling tools 

• Experience with configuration management, infrastructure as code, CI/CD, GitOps, or immutable infrastructure practices 

• Experience supporting LLM training, fine-tuning, retrieval-augmented generation, model serving, or inference platforms