Senior MLOps Engineer

Description

This Department of War enterprise data and analytics program delivers mission‑critical capabilities that enable leaders across the Department to make faster, better-informed decisions using trusted data dos Digital Modernization sector is seeking an experienced Senior MLOps Engineer to support the delivery, enhancement, and adoption of enterprise data and analytics products used across multiple DoD organizations.

In this role, you will work alongside government partners, engineers, and other industry teammates to translate operational and strategic requirements into scalable, production-ready solutions. You will contribute directly to product planning, execution, and continuous improvement—helping ensure capabilities are delivered efficiently, aligned to mission priorities, and positioned for sustained success.

This position offers the opportunity to work on a high‑visibility, enterprise program at the intersection of data, analytics, and emerging AI technologies. Ideal candidates are motivated by mission impact, comfortable operating in complex stakeholder environments, and interested in building deep domain expertise while delivering capabilities with real-world national security outcomes.

• Design, build, and maintain scalable machine learning pipelines for model deployment, validation, monitoring, and lifecycle management.
• Implement model versioning, drift detection, and continuous retraining workflows to ensure model accuracy and compliance.
• Collaborate with data scientists, platform engineers, and security teams to ensure reliable, secure, and efficient delivery of AI/ML capabilities.
• Develop and maintain systems engineering and cybersecurity artifacts for the System.
• Prepare, maintain, and execute a System Engineering Plan (SEP) for managing all systems architecture and system engineering related aspects of the program.
• Conduct systems engineering activities required to specify, build, and maintain system engineering designs for the System.
• Design, engineer, integrate, and continuously improve the underlying infrastructure of the System including cloud environment, network, data storage, logging, and auditing functions.
• Define, document, maintain, and promulgate APIs and technical standards for using and interoperating within and outside the System.
• Establish and maintain integrations with external model providers, making their available models accessible via API.
• Provide Tier‑4 support for any critical issues with the available services and products, in accordance with defined SLAs.
• Design, architect, engineer, and continuously improve all aspects of cybersecurity elements of the System.
• Perform site reliability engineering to build and maintain a reliable, scalable, and efficient System by applying software engineering principles to operational tasks.
• Participate in the Engineering Control Board (ECB) process for supporting all major engineering milestones and decisions for the program.

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Minimum of 8 years of experience in machine learning operations (MLOps) or related fields.
• Experience with cloud platforms that host and manage infrastructure such as AWS, Azure, or Google Cloud.
• Proficiency in programming languages such as Python, Java, or C++.
• Experience with containerization and orchestration tools like Docker and Kubernetes.
• Strong understanding of machine learning model lifecycle management.
• Experience with CI/CD pipelines and version control systems like Git.
• Top Secret clearance required to start.
• Strong problem‑solving skills and ability to work in a collaborative environment.

• Master’s degree in Computer Science, Engineering, or a related field.
• TS/SCI with CI Poly clearance.
• Experience with AI/ML frameworks such as Tensor Flow, PyTorch, or scikit-learn.
• Familiarity with DoD standards, reference designs, and policy.
• Experience with cybersecurity practices and compliance.
• Knowledge of Dev Sec Ops  practices and tools.
• Experience with site reliability engineering (SRE) principles.
• Strong communication and documentation skills.

$ – $ – General guideline only, not a guarantee of compensation. Actual offer may depend on responsibilities, education, experience, skill set, internal equity, market data, and applicable law.

All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.