Machine Learning Operations (MLOps) Engineer

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., * 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.

Physical Demands:

Sedentary work performed in a normal office environment; exerts up to 10 pounds of force occasionally and/or negligible amount of force frequently or constantly to lift, carry, push, pull or otherwise move objects, including the human body. Ability to attend meetings both on and off campus. Spending long hours in front of a computer screen.

Machine Learning, Machining Operations, Research Laboratory, Intelligence Analysis, Machine Learning, Artificial Intelligence (AI), Government, Internet Security, Research & Development (R&D), Scientific Research, * Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.

• 3-6 years of experience in software engineering, data engineering, or MLOps.
• Experience with ML frameworks (e.g., PyTorch, TensorFlow) and pipeline tools (e.g., Airflow, Kubeflow).
• Proficiency in Python and experience with containerization (Docker) and orchestration (Kubernetes).
• Experience with cloud platforms (AWS, Azure, or GCP) and ML services.
• Understanding of software engineering best practices (CI/CD, testing, version control).

Preferences:

• Experience deploying ML systems in regulated or security-sensitive environments.
• Familiarity with data governance, model auditing, and explainability techniques.
• Experience with distributed training, GPU acceleration, and large-scale data systems.
• Knowledge of infrastructure-as-code (Terraform, CloudFormation).
• Experience supporting national security, defense, or intelligence-related programs.
• Active U.S. security clearance.

Work Environment & Impact:

• Work on cutting-edge AI/ML systems addressing real-world national security challenges.
• Collaborate with leading experts across disciplines in a highly innovative R&D environment.
• Help transition advanced research into operational capabilities with tangible mission impact.

Licenses/ Certifications: N/A

Offers of employment are contingent on completion of a background check. Information reported by the background check will not automatically disqualify anyone from employment. Before any adverse decision, the finalist will have an opportunity to provide information to the University regarding disclosable background check information. The University reserves the right to rescind the offer of employment or otherwise decline or terminate employment if the information reported by the background check is deemed incompatible with the position, regardless of when the background check is completed.

Employment Eligibility

The successful candidate must complete employment eligibility verification (on Form I-9) by presenting documents that establish identity and work authorization within the timeframe required by federal immigration law, and where applicable, to demonstrate renewed employment authorization. Failure to complete employment eligibility verification or reverification within the timeframe set forth by law may result in suspension or termination of employment.

EEO Statement

The University of Maryland, College Park is an Equal Opportunity Employer. All qualified applicants will receive equal consideration for employment. Please read the University’s Equal Employment Opportunity Statement of Policy.

Title IX Non-Discrimination Notice

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