Reliability Engineer Jobs in Frederick, MD (NOW HIRING)

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 experiencedSeniorMLOpsEngineerto 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 domainexpertisewhile delivering capabilities with real-world national security outcomes.

Primary Responsibilities:

• Design, build, andmaintainscalable 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 andmaintainsystems 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, andmaintainsystem 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, andpromulgateAPIs and technical standards for using and interoperating within and outside the System.

• Establish andmaintainintegrations 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 withdefined SLAs.

• Design, architect, engineer, and continuously improve all aspects of cybersecurity elements of the System.

• Perform site reliability engineering to build andmaintaina 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 infrastructuresuch as AWS, Azure, or Google Cloud.

• Proficiencyin 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 clearancerequiredto 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, referencedesigns, and policy.

• Experience with cybersecurity practices and compliance.

• Knowledge ofDevSecOpspractices and tools.

• Experience with site reliability engineering (SRE) principles.

• Strong communicationand documentation skills.