À propos de ce poste Lead AI Software Developer chez Leidos
Leidos is seeking a Lead AI Software Developer 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. As part of a large-scale Scaled Agile (SAFe) software development organization, you will serve as a technical leader and strategic architect, shaping enterprise data solutions that bridge mission needs, systems engineering rigor, and modern Agile delivery.
In this position, you will design and implement software solutions independently while working closely with systems engineering, product delivery, and DevSecOps teams. The primary focus is on developing microservices-based applications within an Agile/SAFe environment. Experience with AI-enabled or data-driven systems is advantageous. This position supports government programs and requires the ability to obtain and maintain a favorable Public Trust investigation.
This role focuses on building and modernizing real-time, safety-critical systems using a combination of traditional systems engineering and AI-augmented software development practices. You’ll work on software that directly supports national air traffic operations, applying AI tools to accelerate development, improve quality, and enhance system reliability.
This position is located in either Gaithersburg, MD or Egg Harbor Township, NJ. 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 working from home.
What You’ll Do:
Design, develop, and maintain microservices-based applications using modern software engineering best practices
Collaborate with product owners, architects, and engineers in an Agile/SAFe environment
Participate in sprint planning, daily standups, reviews, and retrospectives
Collaborate with systems engineering and program teams to translate requirements into working software solutions
Develop clean, maintainable, and testable code following CI/CD and DevSecOps practices
Contribute to system design discussions and technical decision-making
Integrate services with databases, messaging systems, and external APIs
Support troubleshooting, performance tuning, and continuous improvement
Work closely with QA, DevOps, and platform teams to ensure reliable delivery
Implement modern automated testing strategies, including unit, integration, contract, and end-to-end testing
Use AI-assisted development tools to accelerate coding, refactoring, debugging, and test creation
Core Technical Qualifications:
Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent experience)
4+ years of relevant experience or a Master's Degree and 2+ years of relevant experience
Hands-on experience building and supporting microservices-based systems
Proficiency in at least one modern programming language (e.g., Java, Python, C++, or similar on a Linux-based system)
Effectively leverage AI-assisted engineering tools to understand unfamiliar codebases, analyze system behavior, develop implementation strategies, accelerate development, and improve software quality.
Experience or exposure to AI/ML-enabled systems (Claude-Code), automation, or intelligent platforms.
Ability to analyze and understand large, complex software systems with many interacting components.
Analyze existing software interfaces and data flows to implement new capabilities within a complex distributed system.
Experience enhancing and maintaining existing software systems while preserving operational stability.
Ability to abstract complex implementation details into higher-level architectural concepts and communicate those concepts effectively.
Experience working in Agile environments
Understanding of APIs and distributed system concepts
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:
Experience with large-scale distributed systems
Experience working with safety-critical or high-availability software
Experience reverse-engineering existing software designs
Experience integrating with legacy systems
Experience reading and understanding large existing codebases
Experience with Git-based version control and CI/CD pipelines
Familiarity with Scaled Agile Framework (SAFe)
Experience working with relational and/or NoSQL databases
Hands-on experience with Kafka or other event-streaming technologies
Cloud experience (AWS, Azure, or GCP)
Containerization and orchestration experience (Docker, Kubernetes)
Experience working in DevSecOps environments
Experience with test automation frameworks and integration of automated test suites into CI/CD pipelines
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.
Original Posting:
July 20, 2026For 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.
Pay Range:
Pay Range $87,100.00 - $157,450.00The 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.
