The Leidos Chief Data & Analytics Office (CDAO) is a high-growth organization at the center of the company's technology strategy. Our Operational AI (Ops.AI) division is seeking a motivated and talented Principal AI/ML Engineer to join our team. This role is critical for transforming innovative AI/ML models into the robust, production-ready solutions that power our nation's most mission-critical applications.

This is an exciting opportunity for a hands-on engineer who excels at bridging the gap between data science and software engineering. You will be a technical leader responsible for the entire lifecycle of our AI/ML models; from design and training to deployment, optimization, and monitoring. You will work with a team of experts to build the scalable, high-performance, and trusted AI systems that help Leidos accelerate innovation and improve mission outcomes.

Primary Responsibilities

• Lead the secure design, training, and deployment of a wide range of AI/ML models, ensuring they meet stringent performance, scalability, and security requirements for mission-critical applications.
• Architect and manage secure automated MLOps pipelines for model monitoring, retraining, and lifecycle management to ensure continuous delivery and operational reliability.
• Drive the optimization of model performance, scalability, and resource consumption in production cloud and on-premise environments.
• Collaborate closely with data scientists, software engineers, and systems architects to translate model prototypes into hardened, production-grade solutions.
• Champion software engineering best practices, including robust version control, comprehensive automated testing, and mature CI/CD processes.
• Provide expert guidance and mentorship to other engineers on MLOps, software development, and operational best practices.
• Stay current with industry trends in MLOps and operational AI to continuously evolve the team's capabilities and technical strategy.

Basic Qualifications

• A Bachelor's degree in Computer Science, Engineering, or a related quantitative field with 12+ years of professional experience, or a Master's degree with 10+ years of relevant experience.
• Demonstrated programming proficiency in Python and hands-on experience with major ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with software engineering best practices and tools, including version control, automated testing, and CI/CD pipelines.
• Solid understanding of the full machine learning lifecycle, from data preparation and model training to deployment and monitoring.
• A understanding of cybersecurity principles as they apply to AI systems, including threat modeling and vulnerability assessment.
• Must be a U.S. Citizen and have the ability to obtain and maintain a U.S. security clearance.

Preferred Qualifications

• Experience with MLOps platforms such as MLflow, Kubeflow, or AWS Sagemaker.
• Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Familiarity with Infrastructure-as-Code (IaC) tools like Terraform or CloudFormation.
• Experience with large-scale data processing tools (e.g., Apache Spark).
• Hands-on experience with a major cloud platform (AWS, Azure, or GCP).
• Knowledge of AI ethics, responsible AI practices, and federal compliance standards (e.g., NIST, CMMC).
• Knowledge of AI security frameworks such as MITRE ATLAS, and the NIST AI Risk Management Framework (AI RMF).
• Contributions to open-source ML projects.

At Leidos, we don’t want someone who "fits the mold"—we want someone who melts it down and builds something better. This is a role for the restless, the over-caffeinated, the ones who ask, “what’s next?” before the dust settles on “what’s now.”

If you’re already scheming step 20 while everyone else is still debating step 2… good. You’ll fit right in.

Original Posting:

Pay Range:

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.