Senior HPC DevOps Engineer

EPAM is a global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and startups. The company has over thirty years of expertise in custom software, product, and platform engineering, enabling clients to become AI-Native enterprises and drive measurable value from innovation and digital investments.

• 5+ years of experience in HPC or DevOps engineering roles
• Knowledge of MPI, OpenMP, and multi-node GPU communication protocols including NCCL and GPUDirect
• Proven experience managing AWS GPU instance families (P-series, G-series, 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 profiling applications with tools like NVIDIA Nsight
• Experience deploying or scaling HPC workloads on cloud infrastructure using EFA, ParallelCluster, and parallel storage solutions
• Proficiency in English at B2+ level

• Guide scientists and data teams in using the platform UI for self-service workloads
• Advise users on infrastructure capacity optimization regarding cost, quotas, and resource availability
• Maintain automated pipelines for infrastructure provisioning and platform deployments
• Resolve technical queries on job scheduling, cluster bottlenecks, and resource quotas
• Collaborate with developer experience teams to improve documentation
• Collaborate with engineering teams to monitor GPU utilization with tools like CloudWatch and Prometheus
• Manage AWS GPU instance families and allocate compute resources for large-scale ML 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 using parallel storage and networking

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