At Leidos, you'll contribute to AI solutions that serve critical national and global missions-ranging from defense and intelligence to healthcare, energy, and space exploration. Our work emphasizes Trusted Mission AI: systems that are transparent, ethical, resilient, and accountable. You'll collaborate with multidisciplinary teams to transition AI research into operational environments where accuracy, security, and reliability are non-negotiable. Joining Leidos means applying your expertise to solve some of the most complex and meaningful challenges of our time.
We are looking for a motivated Senior Machine Learning (MLOps) Engineer to work on challenging problems in a variety of domains - including enterprise IT, health, defense, intelligence, and energy - to get results that apply and go beyond the state of the art for measurably better outcomes. We apply our knowledge, capabilities, and experience to develop and deploy Trusted Mission AI - AI that deserves to be trusted by system owners, end users, and the public - to be helpful, harmless, and honest.
We are looking for an individual to provision, operate, and maintain the CI/CD pipelines and infrastructure for the development and deployment AI Agents.
This role requires a strong foundation in Machine Learning, experience with DevOps/MLOps tools, CI/CD processes, Python programming experience, and the ability to work in fast-paced, Agile development teams.
To be successful in this role, you should be highly motivated and collaborative, working well independently and within a team of junior and senior engineers & researchers.
Primary Responsibilities
The ML-Ops Engineer will collaborate with Agentic AI Scientists to build and securely deploy AI agents to automate and optimize labor intensive workflows. As a member of the Leidos AI Accelerator, you will be tasked to support both R&D tasks and direct customer engagements to speed the transition delivery of novel applied research solutions onto direct contracts.
Tasks include:
- Design, implement, and maintain tools that enable agent deployments using MLOps best practices in scalable cloud infrastructure
- Develop and document processes that enable secure automated development and deployment of AI agents
- Design, build, train, and evaluate Machine Learning models
- Build repeatable Machine Learning pipelines for model training, evaluation, deployment, and monitoring
- Perform R&D to enable AI Observability and performance metrics
- Design, implement, and manage cloud resources for MLOps infrastructure
- Operationalize production AI/ML systems by implementing model serving, monitoring, data and model drift detection, logging, and lifecycle management to ensure reliability, scalability, and maintainability.
- Work in a team of AI/ML researchers and engineers using Agile development processes
Multiple openings at various levels. The various position's minimum education and experience requirements are as follows:
- T2: Bachelor's degree in Computer Science, Engineering or related field and 2+ years of relevant experience, or a Masters degree with relevant experience
- T3: Bachelor's degree with 4+ years of experience or Master's degree with 2+ years of experience in Computer Science, Machine Learning, Artificial Intelligence, or related discipline.
- T4: Bachelor's degree with 8+ years of experience or Master's degree with 6+ years of experience in Computer Science, Machine Learning, Artificial Intelligence, or related discipline.
- T5: Bachelor's degree with 12+ years of experience or Master's degree with 10+ years of experience in Computer Science, Machine Learning, Artificial Intelligence, or related discipline.
Basic Qualifications
- Hands-on experience on building, automating, and managing AI/ML pipelines, and MLOps capabilities (Kubeflow, MLflow, etc.)
- Advanced Python programming skills
- Experience with AI/ML tools, such as common python packages (e.g., scikit-learn, TensorFlow, PyTorch) and Jupyter notebooks
- Experience with MLOps tools and frameworks, such as Kubeflow, MLflow, DVC, TensorBoard
- Experience with Software Development tools, including Git, containerization technologies (e.g., Docker), CI/CD frameworks
- Experience with automated deployment pipelines for Agentic AI Models
- Competence in troubleshooting and mitigating issues with prototyped and deployed AI
- Demonstrated ability to orchestrate ML pipelines
- Ability and willingness to obtain a Secret security clearance
Preferred Qualifications
- Familiarity with cloud-native ML pipelines (AWS Sagemaker, Azure ML, etc.) or hybrid cloud/on-prem deployments.
- Knowledge of security, compliance, and governance of ML systems (model provenance, data privacy, etc.)
- Experience with AI/ML across a broad range of application domains (e.g., NLP, Computer Vision, time series analysis)
- Experience deploying and using AI Explainability and Monitoring tools
- Experience deploying, managing, and using Kubernetes and Kubeflow clusters
- Experience using Infrastructure-as-Code tools (e.g., Terraform, Ansible, CloudFormation)
- Experience deploying, configuring, and managing DevOps tools (e.g., GitLab, Nexus)
- Ability and willingness to obtain a Top Secret security clearance
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:
February 6, 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.