Senior HPC Cloud Engineer

Accenture Federal Services•Hill Air Force Base, UT

6d•$109,500 - $224,200•Onsite

About The Position

Accenture Federal Services is seeking a Senior Cloud Engineer / HPC specialist to join our team and support our client at Hill AFB in Utah. This senior individual-contributor role owns the design and operation of high-performance compute infrastructure for mission workloads, from cluster architecture through GPU-accelerated job scheduling. You will independently make cluster sizing and architecture decisions, mentor engineers, and partner with data platform and AI/ML teams to ensure infrastructure meets mission demand.

Requirements

5 years’ cloud engineering/infrastructure experience, including 2 years focused on HPC cluster design/operations
Experience with HPC job scheduling systems (Slurm, PBS, or equivalent)
Proficiency with AWS ParallelCluster and/or AWS Batch for cloud-based HPC provisioning
Understanding of parallel computing paradigms (e.g.: MPI, shared-memory, distributed task orchestration)
Working knowledge of DevOps practices (e.g.: CI/CD, infrastructure-as-code, GitOps)
Scripting proficiency in Python, Bash, or PowerShell

Nice To Haves

Bachelor’s in Computer Science, Computational Science, Engineering, or related field (certifications considered in lieu)
Hands-on experience with industry GPU hardware/software ecosystems
Familiarity with CUDA, cuDNN, and GPU-accelerated libraries
Experience designing/operating data pipelines (Kafka, Airflow, Spark)
Familiarity with AWS data services (EMR, Redshift, Glue)
AWS certifications (Solutions Architect or Advanced Networking)

Responsibilities

Design, size, tune, and operate HPC clusters for compute-intensive workloads
Own HPC job scheduling infrastructure (Slurm, PBS, or equivalent)
Architect/manage AWS ParallelCluster and/or AWS Batch for cloud-based HPC provisioning
Design for parallel computing paradigms (MPI, shared-memory, distributed task orchestration)
Configure industry GPU hardware/software for accelerated workloads
Tune CUDA, cuDNN, and GPU libraries for scientific computing/data processing
Optimize scheduling/resource allocation across CPU/GPU node pools
Integrate HPC compute with data pipelines (Kafka, Airflow, Spark) and AWS data services (EMR, Redshift, Glue)
Ensure HPC infrastructure meets security controls/accreditation for classified environments
Mentor engineers building HPC/GPU-compute familiarity
Document cluster architecture decisions, runbooks, and operational procedures