MLOps Engineer

At Leidos youll contribute to AI solutions that serve critical national and global missionsranging from defense and intelligence to healthcare energy and space exploration. Our work emphasizes Trusted Mission AI: systems that are transparent ethical resilient and accountable. Youll 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 positions minimum education and experience requirements are as follows:

• T2: Bachelors degree in Computer Science Engineering or related field and 2 years of relevant experience or a Masters degree with relevant experience
• T3: Bachelors degree with 4 years of experience or Masters degree with 2 years of experience in Computer Science Machine Learning Artificial Intelligence or related discipline.
• T4: Bachelors degree with 8 years of experience or Masters degree with 6 years of experience in Computer Science Machine Learning Artificial Intelligence or related discipline.
• T5: Bachelors degree with 12 years of experience or Masters 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 youre looking for comfort keep scrolling. At Leidos we outthink outbuild and outpace the status quo because the mission demands it. Were not hiring followers. Were recruiting the ones who disrupt provoke and refuse to fail. Step 10 is ancient history. Were already at step 30 and moving faster than anyone else dares.

Original Posting:

Pay Range:

The Leidos pay range for this job level is a general guideline onlyand 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.