Senior HPC Cluster Engineer - AI, ML

Team: IT

Location: India

Commitment: Full-time

Workplace Type: remote

Accountabilities:

• Provide technical leadership for systems administration and service delivery across large-scale AI/HPC infrastructure, coordinating upgrades, incident response, and reliability improvements.
• Own the day-to-day operation of production AI/HPC clusters, monitoring system health, user experience, resource utilization, and adherence to internal service-level targets.
• Build, maintain, and scale heterogeneous AI/ML clusters across on-premises and cloud environments, covering compute, networking, storage, and GPU-accelerated infrastructure.
• Develop scalable automation and tooling to improve the deployment, configuration, management, and operational efficiency of AI/HPC environments.
• Collaborate with global engineering teams and internal users to understand evolving research and computing requirements and deliver reliable infrastructure solutions.
• Support researchers in running complex AI/HPC workloads, including performance analysis, troubleshooting, optimization, and workload tuning.
• Analyze cluster efficiency, job fragmentation, and GPU utilization to identify opportunities to reduce resource waste and improve overall capacity.
• Conduct root cause analysis for infrastructure and service issues, proactively identifying risks and implementing corrective actions before they affect users.
• Lead SEV triage, incident response, and postmortems for reliability events affecting production infrastructure or users.
• Build strong relationships with customers, researchers, and cross-functional engineering teams to improve service delivery and anticipate infrastructure needs.
• Participate in an on-call rotation and provide timely support for critical production GPU clusters.

Requirements:

• Bachelor's degree in Computer Science, Electrical Engineering, or a related discipline, or equivalent practical experience.
• 5+ years of experience designing, deploying, and operating large-scale compute or infrastructure environments.
• Strong experience with AI/HPC job schedulers such as Slurm, Kubernetes, PBS, RTDA, BCM, or LSF.
• Proficiency administering Linux distributions such as CentOS/RHEL and/or Ubuntu.
• Hands-on experience with cluster configuration and infrastructure management tools including BCM, Terraform, Ansible, Puppet, Salt, or similar technologies.
• Strong knowledge of container technologies such as Docker, Singularity, Podman, Shifter, or Charliecloud.
• Proficiency with Python programming and Bash scripting for infrastructure automation and operational tooling.
• Practical experience supporting AI/HPC workflows that use MPI.
• Experience analyzing and tuning the performance of diverse AI and HPC workloads.
• Strong troubleshooting, analytical, and root-cause analysis skills, with the ability to resolve complex distributed infrastructure problems.
• Excellent collaboration and communication skills, particularly when working with researchers, infrastructure engineers, and globally distributed teams.
• Passion for continuous learning and for staying current with emerging technologies and best practices in HPC and AI/ML infrastructure.

Nice-to-have experience:

• Experience working with NVIDIA GPUs, CUDA programming, NCCL, or MLPerf benchmarking.
• Understanding of AI/ML concepts, algorithms, models, and frameworks such as PyTorch or TensorFlow.
• Experience with InfiniBand, IPoIB, and RDMA technologies.
• Knowledge of high-performance distributed storage platforms such as Lustre or GPFS.
• Experience with advanced GPU cluster optimization and large-scale AI/ML infrastructure.

Benefits:

• Opportunity to work on large-scale AI and high-performance computing infrastructure supporting advanced research and engineering workloads.
• Exposure to GPU-accelerated computing, distributed systems, cloud infrastructure, HPC, and emerging AI/ML technologies.
• Collaboration with highly skilled engineering and research teams across global locations.
• Opportunities to influence infrastructure architecture, automation, reliability, and operational best practices.
• Continuous learning and exposure to rapidly evolving technologies in AI, ML, and high-performance computing.
• Flexible work arrangements may be available, with opportunities based in Bengaluru, Pune, or remote within India.
• Full-time role within a technically advanced and innovation-focused environment.
• Opportunity to contribute to infrastructure that enables next-generation AI and scientific computing.