<div class="content-intro"><div> <div> </div> <div>At Accenture Federal Services, nothing matters more than helping the US federal government make the nation stronger and safer and life better for people. Our 13,000+ people are united in a shared purpose to pursue the limitless potential of technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations. </div> <div> </div> <div>Join Accenture Federal Services, a technology company within global Accenture. Recognized as a Glassdoor Top 100 Best Place to Work, we offer a collaborative and caring community where you feel like you belong and are empowered to grow, learn and thrive through hands-on experience, certifications, industry training and more. </div> <div> </div> <div>Join us to drive positive, lasting change that moves missions and the government forward!</div> <div> </div> </div></div><p>Accenture Federal Services is seeking an AI Cloud Engineer to join our team and support our client at Hill AFB in Utah. This role builds and operates the cloud infrastructure that supports AI/ML platform capabilities — GPU-enabled Kubernetes, data lake and streaming analytics infrastructure, and model registry/serving environments — for mission AI/ML workloads. You will work alongside AI/ML architecture and data platform teams, translating platform requirements into concrete, secure cloud infrastructure that satisfies Zero Trust and AI risk management expectations for a defense environment. The ideal candidate brings hands-on cloud engineering experience with exposure to containerized AI/ML infrastructure and is comfortable operating in a fast-paced, mission-focused environment.</p> <p><strong>What you’ll do: </strong></p> <p><em>AI/ML Infrastructure Engineering </em></p> <ul> <li>Build and operate GPU-enabled Kubernetes infrastructure supporting AI/ML training and inference workloads</li> <li>Support data lake and streaming analytics infrastructure feeding AI/ML pipelines</li> <li>Support model registry and model-serving infrastructure for AI/ML platform capabilities</li> <li>Implement Infrastructure as Code for AI/ML infrastructure components (Terraform, Ansible)</li> </ul> <p><em>Security & Compliance </em></p> <ul> <li>Implement cloud security controls aligned with Zero Trust Architecture (NIST 800-207) principles for AI/ML infrastructure</li> <li>Support compliance activities relevant to AI risk management (NIST AI RMF) and CMMC requirements as they apply to AI/ML infrastructure</li> <li>Support STIG compliance checks and vulnerability remediation on AI/ML platform components</li> </ul> <p><em>Platform Operations </em></p> <ul> <li>Monitor and troubleshoot GPU-enabled compute and data pipeline infrastructure</li> <li>Support CI/CD pipeline operations for AI/ML model deployment workflows</li> <li>Collaborate with data platform and AI/ML architecture teams on infrastructure requirements</li> </ul> <p><em>Agile Delivery & Collaboration </em></p> <ul> <li>Operate within an Agile framework — participate in sprints, standups, and retrospectives</li> <li>Document infrastructure decisions, runbooks, and operational procedures</li> <li>Support cross-training and knowledge sharing with the broader Develop & Build team</li> </ul> <p><strong>What you’ll need: </strong></p> <ul> <li>4 years of cloud engineering or infrastructure experience</li> <li>Working knowledge of Kubernetes, including GPU-enabled node pools</li> <li>Familiarity with data lake and/or streaming analytics concepts</li> <li>Familiarity with Zero Trust Architecture principles (NIST 800-207)</li> <li>Working knowledge of Infrastructure as Code (Terraform, Ansible, or CloudFormation)</li> <li>Scripting proficiency in Python, Bash, or PowerShell</li> </ul> <p><strong>Bonus Points if you have: </strong></p> <ul> <li>Bachelor's degree in Computer Science, Data Engineering, Engineering, or related technical field (relevant certifications considered in lieu of degree)</li> <li>Exposure to containerized AI/ML infrastructure (GPU-enabled Kubernetes, model serving) or a strong willingness to develop this expertise quickly</li> <li>Familiarity with model registry and model-serving tooling</li> <li>Familiarity with NIST AI Risk Management Framework (AI RMF) concepts</li> <li>Experience with data pipeline tools (Apache Kafka, Apache Airflow, Apache Spark)</li> <li>AWS or Azure AI/ML-focused certifications</li> <li>CompTIA Security+ (current) or equivalent security certification</li> </ul> <p><strong>Clearance: </strong></p> <ul> <li>Must have an active Secret level clearance; Top Secret preferred </li> </ul> <p><em>Work Environment & Culture Fit </em></p> <ul> <li>Comfortable operating in a centralized vision, decentralized command culture</li> <li>Strong Agile framework familiarity — ability to work effectively in sprints, standups, and retrospectives</li> <li>Solution ownership mindset — takes responsibility for assigned AI/ML infrastructure components</li> <li>Fail-fast, fail-forward mindset — comfortable learning from mistakes and iterating quickly</li> <li>Growth-oriented — eager