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.

Success in This Role Means:

• HPC clusters are right-sized and cost-efficient, scaling compute and GPU resources to workload demand
• Job scheduling (Slurm, PBS, AWS Batch/ParallelCluster) is reliable and self-service, minimizing manual intervention
• GPU-accelerated workloads run efficiently, with proactive CUDA/cuDNN tuning and resource allocation
• Data pipeline teams (Kafka, Airflow, Spark, EMR) have a stable, well-documented compute foundation
• Security and accreditation requirements for classified HPC workloads are met without impeding mission delivery

What you’ll do:

• 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

What you’ll need:

• 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

Bonus Points if you have:

• 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)

Clearance:

• Must have an active Secret clearance; Top secret preferred

Work Environment & Culture Fit

• Comfortable in fast-paced, dynamic environments with evolving compute demands
• Strong Agile framework familiarity (sprints, standups, retrospectives)
• Solution ownership mindset—responsible for cluster efficiency/reliability
• Fail-fast, fail-forward mentality—iterates on cluster tuning
• Growth-oriented—invested in mentoring engineers

Who Thrives in This Role

• Extreme ownership mindset—accountable for cluster efficiency/workload reliability
• Comfortable with ambiguity—translates vague mission needs into concrete architectures
• Detail-oriented on performance—tunes resource allocation to avoid over-provisioning
• Strong collaborator—partners with data engineering and AI/ML teams
• Self-directed—proactively identifies scaling/tuning opportunities

Why This Role Matters

Directly determines whether mission workloads—simulation, modeling, AI/ML pipelines—have the capacity and performance needed. Work is visible at every level, from data engineers and AI/ML teams to mission stakeholders relying on timely simulation and analysis results.