Senior HPC DevOps Engineer

EPAM Systems is a global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and startups. They have over thirty years of expertise in custom software, product and platform engineering, empowering clients to become AI-Native enterprises. The position is within the Science, Innovation & Labs team, focusing on HPC and machine learning operations platform.

• 5+ years of experience in HPC or DevOps engineering roles
• Knowledge of MPI, OpenMP, and multi-node GPU communication protocols such as NCCL and GPUDirect
• Proven experience managing AWS GPU instance families, including P-series, G-series, and Tranium/Inferentia
• Hands-on mastery of AWS Capacity Blocks for ML, On-Demand Capacity Reservations (ODCRs), and Service Quota management
• Experience deploying containerized environments using Apptainer/Singularity, Docker, or Enroot
• Understanding of I/O performance bottlenecks with distributed file systems such as Lustre, GPFS, BeeGFS, or AWS FSx for Lustre
• Hands-on skill in profiling applications using NVIDIA Nsight or similar tools to locate memory and compute bottlenecks
• Experience deploying or scaling HPC workloads on cloud infrastructure utilizing EFA, ParallelCluster, and parallel storage (FSx for Lustre)
• Proficiency in English at a B2+ level

• Guide scientists and data teams in navigating and utilizing the platform user interface (UI) effectively, enabling self-service workloads without infrastructure friction
• Advise users and manage infrastructure capacity, optimizing cost, quotas, and resource availability for heavy workloads
• Maintain automated pipelines for infrastructure provisioning and platform service deployments
• Resolve technical queries related to job scheduling failures, cluster bottlenecks, and resource quotas
• Collaborate with developer experience teams to improve documentation
• Collaborate with engineering teams to monitor GPU utilization using tools such as CloudWatch or Prometheus
• Manage AWS GPU instance families and allocate block compute for large-scale ML training and inference pipelines
• Ensure compute availability through capacity planning and reservation management
• Deploy containerized environments tuned for HPC and GPU pass-through
• Deploy and scale HPC workloads on cloud infrastructure utilizing parallel storage and networking solutions

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