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Leidos

Leidos is an innovation company rapidly addressing the world’s most vexing challenges in national security and health.

Principal AI/ML Engineer

Machine Learning EngineerMachine Learning EngineerOtherRemoteTeam 10,001+Since 1969H1B SponsorCompany SiteLinkedIn

Location

United States

Posted

94 days ago

Salary

0

No structured requirement data.

Job Description

Principal AI/ML Engineer

Leidos

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description 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. - 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. 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. Requirements - 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. Company Description 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 January 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 $131,300.00 - $237,350.00 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.

Job Requirements

  • 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.
  • 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.

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