Principal Responsible AI Engineer

Leidos

About The Position

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 Responsible AI Engineer to join our team. This role is critical for ensuring the ethical, transparent, and secure development of AI that will power our nation's most mission-critical applications. Building trust into our AI systems is vital to accelerating innovation, technology adoption, and improving mission outcomes. This is an exciting opportunity for a hands-on builder who excels at integrating Responsible AI principles into production-ready solutions. You will be a key technical contributor responsible for the entire lifecycle of our Responsible AI initiatives, from design and development to deployment and monitoring. You will work with a team of experts to build the scalable, high-performance, and trustworthy AI systems that are essential for our success.

Requirements

A Bachelor's degree in Computer Science, Engineering, or a related quantitative field with 4+ years of professional experience, OR a Master's degree with 2+ 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 strong understanding of Responsible AI principles, ethical AI practices, and techniques for bias detection and mitigation.
An 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.

Nice To Haves

Experience working within the national security, defense, or intelligence communities.
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.
Hands-on experience with a major cloud platform (AWS, Azure, or GCP).
Knowledge of AI ethics, responsible AI practices, and federal compliance standards.
Deep familiarity with AI governance and security frameworks such as the NIST AI Risk Management Framework (AI RMF) and MITRE ATLAS.

Responsibilities

System Development: Develop and implement enterprise-scale Responsible AI systems and governance frameworks, ensuring they meet the ethical, performance, and security requirements for mission-critical applications.
Framework Architecture: Contribute to the architecture and implementation of a centralized "Responsible AI Framework" to ensure compliance, manage model access, and provide a unified interface for governance and risk management.
Monitoring and Auditing: Implement and manage robust monitoring systems to track model performance, fairness, bias, and ethical compliance, and to optimize the cost-effectiveness of AI systems in production.
Strategic Collaboration: Work closely with principal engineers, data scientists, and systems architects to translate strategic designs into hardened, production-grade solutions that embed fairness, accountability, and transparency principles.
Governance and Guardrails: Establish and maintain robust AI governance frameworks and guardrails to ensure data integrity, filter inputs/outputs, prevent bias, mitigate deployment risks, and protect against adversarial attacks.
Best Practices: Apply and promote software engineering best practices, including robust version control, comprehensive automated testing, and mature CI/CD processes for AI systems.
Continuous Learning: Stay current with industry trends in Responsible AI, Explainability (XAI), operational AI, and MLOps to continuously evolve the team's capabilities and technical implementation.