AI Engineering Lead

Description

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.

What You’ll Do

• 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 / Desired Qualifications

• Aviation or safety-critical AI/ML application experience

• Responsible AI frameworks and governance implementation

• LLM integration and prompt engineering for enterprise applications

• Computer vision or NLP application development

• Real-time inference and low-latency ML serving

• Kubernetes-based ML workload orchestration

• AI governance and regulatory compliance frameworks

• Experience differentiating AI/ML development pipelines for internal tooling versus safety-critical operat…