Sr. AI Infrastructure Engineer

The Leidos Digital Modernization Sector is seeking a highly motivated Senior AI Infrastructure Engineer; this position will allow for full time telework from any U.S. based location.

POSITION SUMMARY:
The Leidos Chief Data & Analytics Office (CDAO) is a new, high-growth organization at the center of the company's technology strategy. Our Operational AI (Ops.AI) division is seeking an experienced and visionary Senior AI Infrastructure Engineer to join our team. This role is essential for designing the secure, scalable, and trusted AI infrastructure that underpins our mission-critical solutions. This is an exciting opportunity to leverage your experience to empower Leidos with responsible AI capabilities that accelerate innovation and improve mission outcomes.

PRIMARY RESPONSIBILITIES:
The Senior AI Infrastructure Engineer will serve as the lead for developing and deploying AI platforms at scale across the enterprise. This individual will be responsible for constructing and maintaining the high-performance, scalable infrastructure that our data scientists and AI/ML engineers rely on. This role is perfect for a technical expert who is passionate about creating robust, efficient, and automated environments for developing and deploying cutting-edge AI at an enterprise scale.

• Infrastructure Deployment: Build and maintain scalable, reliable, and secure AI infrastructure using modern cloud and on-premise technologies.
• AI Tools & Pipelines: Implement and manage AI/MLOps tools and CI/CD pipelines for AI model usage, training, deployment, and monitoring.
• Business Continuity: Ensure the high availability, reliability, and performance of all AI platforms and systems.
• Stakeholder Collaboration: Collaborate with data scientists, engineers, and program managers to ensure architectural designs meet project requirements and strategic objectives.
• Compliance & Security: Ensure mission assurance and full compliance with AI lifecycle management frameworks, including AI risk management and security protocols.
• Technology Evaluation: Evaluate and recommend new technologies, tools, and frameworks to enhance our AI capabilities and maintain our competitive edge.
• Mentorship: Provide expert guidance and mentorship to development teams on architectural patterns, implementation, and best practices.

BASIC QUALIFICATIONS:

• Bachelor's degree in Computer Science, Engineering, or a related technical field with 12+ years of professional experience; additional years of experience may be substituted in lieu of a degree. 
• Strong, hands-on experience with cloud services (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform, Ansible).
• Expert-level understanding of MLOps principles, CI/CD pipelines, and infrastructure-as-code (IaC).
• Proficiency in scripting languages such as Python or Bash
• Substantial understanding of cybersecurity principles as they apply to AI systems.
• This position offers flexibility for remote or hybrid work arrangements.
• Must be a U.S. Citizen and able to obtain and maintain a security clearance.

PREFERRED QUALIFICATIONS:

• Experience deploying and managing AI/ML platforms (e.g., Kubeflow, MLflow, Sagemaker).
• Deep experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• DevOps or Cloud certifications (e.g., AWS Certified DevOps Engineer, Certified Kubernetes Administrator).
• Professional certifications in cloud architecture (e.g., AWS Certified Solutions Architect - Professional, Azure Solutions Architect Expert).
• Experience with GPU-accelerated computing.
• Experience with trusted AI frameworks and AI risk management.
• Familiarity with defense and federal compliance standards such as DoD IL, FedRAMP, NIST, or CMMC.
   

Come break things (in a good way). Then build them smarter.

We're the tech company everyone calls when things get weird. We don’t wear capes (they’re a safety hazard), but we do solve high-stakes problems with code, caffeine, and a healthy disregard for “how it’s always been done.”

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.