Senior MLOps Engineer

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 at scale. Leidos 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.

Primary Responsibilities:

• 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.

Basic Qualifications:

• 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.

Preferred Qualifications:

• Master's degree in Computer Science, Engineering, or a related field.

• TS/SCI with CI Poly clearance.

• Experience with AI/ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

• Familiarity with DoD standards, refer ence designs, and policy.

• Experience with cybersecurity practices and compliance.

• Knowledge of DevSecOps practices and tools.

• Experience with site reliability engineering (SRE) principles.

• Strong communication and documentation skills.

If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares.

Original Posting:
March 2, 2026

For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:
Pay Range $107,900.00 - $195,050.00

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.