MLOps Engineer

Description

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 Dev Ops/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.

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

• 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.

• 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, Tensor Flow, PyTorch) and Jupyter notebooks
• Experience with MLOps tools and frameworks, such as Kubeflow, MLflow, DVC, Tensor Board
• Experience with Software Development tools, including Git, containerization technologies (e.g., Docker), CI/CD frameworks
• Experience…