Machine Learning Operations (MLOps) Engineer

NLP PEOPLE; United States; On-site; USD 85,000 - 120,000; Full time

Job summary

NLP PEOPLE is looking for candidates who can join the Applied Research Laboratory for Intelligence & Security (ARLIS) to design, maintain, and operationalize machine learning pipelines in secure environments. This position requires a Bachelor's degree and relevant experience in software engineering or data engineering.

The ideal candidate will collaborate with researchers and industry partners while adhering to best practices in machine learning security and deployment.

Qualifications

• 3–6 years of experience in software engineering, data engineering, or MLOps.
• Understanding of software engineering best practices is required.
• Must be able to obtain a U.S. security clearance.

Responsibilities

• Design and maintain scalable ML pipelines for training and deployment.
• Operationalize ML models in production-grade environments.
• Implement CI/CD workflows for ML systems.

Skills

Machine Learning frameworks (PyTorch, TensorFlow)

Data Engineering

Python

Containerization (Docker)

CI/CD practices

Cloud platforms (AWS, Azure, GCP)

Education

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

Tools

Kubernetes

Airflow

Kubeflow

Job description

Job Overview

The Applied Research Laboratory for Intelligence & Security (ARLIS) at the University of Maryland is a University‑Affiliated Research Center 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.

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).

Minimum Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a 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).
• Must be able to obtain a U.S. security clearance and meet the requirements for access to classified information.

Preferred Qualifications

• 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 & Physical Demands

Work is primarily sedentary in a normal office environment. Tasks include lifting or moving objects up to 10 pounds occasionally or maintaining a negligible amount of force frequently or constantly. Employees will spend long hours in front of a computer screen and may attend meetings both on and off campus.

Security Clearance

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.

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.