AI Engineering Lead

Leidos is   seeking   an   AI   Engineering   Lead to join the Air Traffic Business Area within the Homeland Sector, supporting the development of the Leidos Common Automation Platform (L-CAP). L-CAP is a mission-critical, future-ready automation platform built on a hybrid cloud data mesh architecture, enabling next-generation air traffic management capabilities. You will lead AI engineering strategy and practices for L-CAP, integrating AI-augmented development tools, machine learning capabilities, and intelligent automation into the program's engineering workflow. We are building with an AI-first engineering mindset, embracing emerging AI capabilities and modern development practices to accelerate delivery, improve software quality, and continuously evolve how we design and build mission-critical systems. This position supports government programs and requires the ability to obtain and   maintain   a favorable Public Trust investigation.   This role is part of a growing program, and hiring will depend on available funding. We review applications on a rolling basis, but the timeline for interviews and offers may vary. In some cases, we may extend  contingent offers  that become active once funding is confirmed.  This position is located in  Gaithersburg, MD; Egg Harbor Township, NJ; or Eagan, MN . This is an opportunity to contribute to projects that impact millions of air travelers.  This is a hybrid position requiring 3 days onsite and 2 days remote work.  Candidates should reside a commutable distance to one of the above locations.

Lead AI engineering strategy and roadmap development for the L-CAP program   Design and implement   MLOps   pipelines for model development, training, and deployment   Establish AI-augmented development practices across engineering teams   Define model governance including versioning, monitoring, and lifecycle management   Evaluate, pilot, and deploy AI development tools across the engineering organization   Address safety considerations for AI applications in aviation-critical systems   Drive intelligent automation initiatives to improve engineering productivity   Integrate AI/ML capabilities with the L-CAP platform architecture   Mentor engineering teams on AI/ML best practices and responsible AI principles   Distinguish between AI capabilities that improve engineering productivity (developer tools, code generation, testing automation) and AI functionality incorporated into operational ATC services (decision support, anomaly detection, predictive maintenance)   Define separate development practices, validation approaches, and certification considerations for engineering-productivity AI versus operational-mission AI   Ensure alignment between requested AI skill sets and intended program   objectives   across both engineering and operational domains   Champion an AI-first engineering culture by   identifying   and applying AI-assisted development capabilities, automation, and emerging software engineering practices that improve developer productivity, code quality, testing, and delivery.   Core Technical Qualifications:   Bachelor's degree with 8+ years of AI/ML engineering experience ( Master's   preferred)   ML/AI engineering leadership including team and strategy management   MLOps   pipeline design and implementation at enterprise scale   Strong Python   expertise   and ML framework   proficiency   ( PyTorch , TensorFlow)   Model deployment, monitoring, and lifecycle management   Experience with AI-assisted development tools and productivity platforms   Large-scale ML system design and distributed training   Engineering automation and intelligent workflow design   Cloud ML services experience (AWS SageMaker, Azure ML, or equivalent)   ​ Ability to obtain and maintain a Public Trust   U.S. citizenship required Successful completion of background investigations as required by the government customer   Preferred /