Senior AI/ML Engineer

<p>540 is seeking a Senior AI/ML Engineer to support a mission-critical technology modernization effort for the Department of War. You will lead the design and evolution of production AI/ML services and infrastructure that enable teams to develop, deploy, monitor, and scale models supporting complex defense missions.</p> <p>Working with software engineers, data engineers, data scientists, cybersecurity teams, and mission stakeholders, you will translate complex requirements into secure, scalable AI/ML solutions. You will define MLOps standards, guide technical delivery, and establish reusable capabilities supporting the end-to-end machine learning lifecycle.</p> <p><strong>Location</strong>: Arlington, VA<br><strong>Citizenship &amp; Clearance Requirement</strong>: Per client requirements, candidates must be U.S. Citizens with an active DoW Secret (or higher) clearance<br><strong>Education Requirement: </strong>Bachelor’s degree in Computer Science, Engineering, or a related technical field preferred; equivalent combinations of education and relevant experience will be considered<br><strong>540 Internal Thrive Level: </strong>Senior Software Engineer</p> <p><strong>WHY 540?</strong></p> <p>540 is a forward-thinking company that the government turns to in order to #getshitdone. We don’t just talk about innovation – we deliver it. We break down barriers, build impactful technology, and solve mission-critical problems.</p> <p><strong>HOW YOU’LL DRIVE IMPACT</strong></p> <ul> <li>Lead the architecture and evolution of AI/ML services, platforms, and lifecycle capabilities supporting WDP</li> <li>Translate mission requirements into scalable AI/ML architectures and implementation strategies</li> <li>Define MLOps standards, reusable patterns, and best practices across engineering teams</li> <li>Architect automated pipelines for model training, validation, testing, deployment, and monitoring</li> <li>Develop reusable frameworks, libraries, and shared components that accelerate AI/ML delivery</li> <li>Design model-serving platforms supporting secure, scalable, and reliable batch or real-time inference</li> <li>Establish model monitoring, performance tracking, drift detection, explainability, and governance capabilities</li> <li>Define practices for model versioning, artifact management, reproducibility, feature engineering, and data lineage</li> <li>Optimize AI/ML services and infrastructure for performance, scalability, reliability, and cost efficiency</li> <li>Establish CI/CD, infrastructure-as-code, automated testing, and operational practices for AI/ML systems</li> <li>Lead technical reviews and resolve complex issues spanning models, applications, data, infra