Machine Learning Operations (MLOps) Engineer

About the role

The Applied Research Laboratory for Intelligence & Security (ARLIS) at the University of Maryland seeks a mid‑level MLOps Engineer to bridge cutting‑edge AI research and production. You will enable secure, reproducible machine‑learning pipelines that support national‑security missions.

Key responsibilities

• Design, build, and maintain scalable ML pipelines for training, evaluation, and deployment.
• Operationalize 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; create feedback loops and retraining strategies.
• Collaborate with researchers to translate experimental models into production‑ready systems.
• Integrate security best practices (DevSecOps) into AI/ML workflows.
• Support deployment of ML systems in constrained or classified environments.
• Contribute to infrastructure design for AI/ML workloads such as GPU clusters and distributed systems.

Required profile

• Mid‑level experience in MLOps or related fields.
• U.S. citizenship and ability to obtain a Secret security clearance.
• Willingness to undergo background, credit, and security investigations.
• Ability to work in a mission‑critical, classified environment.

Required skills

• Design and implementation of ML pipelines.
• CI/CD for machine‑learning systems.
• Model versioning and feature‑store management.
• Monitoring and drift detection for deployed models.
• DevSecOps practices for AI/ML.
• Experience with GPU clusters and distributed computing environments.