MLOps Engineer: Secure, Production ML Pipelines

University of Maryland; College Park (MD); On-site; USD 150,000 - 225,000; Full time

Job summary

ARLIS at the University of Maryland seeks a mid-level MLOps Engineer to support deployment, scaling, and operationalization of ML systems for national security applications. You will bridge 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, including security-focused

Qualifications

• 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 (PyTorch, TensorFlow) and pipeline tools (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).

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).

Skills

ML frameworks (PyTorch, TensorFlow)

Python

Docker

Kubernetes

Airflow

Kubeflow

CI/CD

Cloud platforms (AWS, Azure, GCP)

Model versioning

Education

Bachelor’s degree in Computer Science, Engineering, Data Science, or related field

Tools

Airflow

Kubeflow

Docker

Kubernetes

Job description

ARLIS at the University of Maryland seeks a mid-level MLOps Engineer to support deployment, scaling, and operationalization of ML systems for national security applications. You will bridge 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, including security-focused