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Leidos isseekingan AI EngineeringLead 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 andmaintaina 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 implementMLOpspipelines 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 programobjectivesacross both engineering and operational domains
Champion an AI-first engineering culture byidentifyingand 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'spreferred)
ML/AI engineering leadership including team and strategy management
MLOpspipeline design and implementation at enterprise scale
Strong Pythonexpertiseand ML frameworkproficiency(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 operational systems
Knowledge of certification requirements for AI systems in aviation or safety-critical operational environments
Why Leidos?
You’ll work on systems where performance, precision, and reliability matter - every second. This is not experimental AI for prototypes. This is disciplined, responsible AI applied to mission-critical software that supports national infrastructure.
If you’re excited by solving complex problems in regulated, real-world environments - and using AI as a force multiplier rather than a shortcut - we’d like to talk.
ATMC
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.
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.
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.