Role OverviewJoin a team accelerating the modernization of a large federal agency's enterprise data and analytics ecosystem. As an ML Ops Engineer, you will develop and maintain machine learning pipelines, model registries, deployment frameworks, monitoring solutions, and automated workflows that support enterprise AI initiatives.
What You Will Do
Design, build, and maintain production-grade MLOps pipelines for model training, evaluation, validation, registration, deployment, monitoring, retraining, rollback, and retirement. Implement experiment tracking, model versioning, model registries, lineage, quality metrics, model cards, and approval workflows using MLflow and Unity Catalog.
Why It Might Be a Fit
Partner with data scientists, AI engineers, data engineers, platform engineers, DevSecOps, Security, and Governance personnel to make models repeatable, testable, deployable, and operationally supportable. Monitor compute and serving utilization, tune cluster and endpoint configurations, apply tagging conventions, and implement cost-optimization strategies for training and inference workloads.
Requirements
- U.S. Citizenship
- Reside in the U.S. and be authorized to work in the U.S.
- 6+ years of experience designing and supporting production machine learning solutions in cloud environments
- Bachelor's degree in Computer Science, Software Engineering, Machine Learning, Data Science, Engineering, or a related field or a high school diploma with four (4) additional years of relevant experience in lieu of a bachelor's degree
Benefits
- Dental insurance
- Vision insurance
- Health insurance
- 401(k) or other retirement plan
- Paid time off
- Life insurance
- Disability insurance
- Employee assistance program
- Flexible work arrangements
- Professional development opportunities
- Paid holidays
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