Machine Learning Operations (MLOps) Engineer

Job Description Summary

Organization's Summary Statement:
The Applied Research Laboratory for Intelligence & Security (ARLIS) at the University of Maryland is a University-Affiliated Research Center (UARC) dedicated to advancing research, innovation, and technology transition to improve decision making for U.S. national security. ARLIS combines deep scientific expertise with operational insight to address challenges in intelligence analysis, cybersecurity, artificial intelligence / machine learning, quantum science, and human-machine teaming. Researchers, scientists, engineers, and analysts at ARLIS collaborate with government agencies, industry partners, and academic institutions to deliver actionable insights and transformative solutions through research and development. Employees at ARLIS work on projects of critical importance, contribute directly to the nation’s security, and are supported by a culture that values integrity, collaboration, and professional growth.

ARLIS is seeking a mid-level MLOps Engineer to support the deployment, scaling, and operationalization of machine learning systems for national security applications. This role focuses on bridging research and production by enabling robust, secure, and reproducible ML pipelines in mission-critical environments. The successful candidate will work closely with AI researchers, software engineers, and domain experts to transition advanced algorithms into operational capabilities.

Key Responsibilities:
-Design, build, and maintain scalable ML pipelines for training, evaluation, and deployment.
-Operationalize machine learning models in secure, production-grade environments (on-prem, cloud, hybrid).
-Implement CI/CD workflows for ML systems, including automated testing, validation, and monitoring.
-Manage data pipelines, feature stores, and model versioning to ensure reproducibility and auditability.
-Monitor model performance, drift, and system health; implement feedback loops and retraining strategies.
-Collaborate with researchers to translate experimental models into production-ready systems.
-Integrate security best practices into ML workflows (DevSecOps for AI systems).
-Support deployment of ML systems in constrained or classified environments.
-Contribute to infrastructure design supporting AI/ML workloads (GPU clusters, distributed systems).

Must be able to obtain a U.S. security clearance. If selected, you must meet the requirements for access to classified information and will be subject to a government security clearance investigation that includes criminal and credit history checks, as well as verification of U.S. citizenship, birth, education, employment, and military history.

Final offer is contingent upon the candidate’s ability to successfully obtain the necessary interim Secret security clearance, as determined by the U.S. Government, prior to commencing employment.