AI Engineer

AI Engineer

Leidos has a new and exciting opportunity for an AI Engineer in our National Security Sector's (NSS) Cyber & Analytics Business Area (CABA). Our talented team is at the forefront in Security Engineering, Computer Network Operations (CNO), Mission Software, Analytical Methods and Modeling, Signals Intelligence (SIGINT), and Cryptographic Key Management. At Leidos, we offer competitive benefits, including Paid Time Off, 11 paid Holidays, 401K with a 6% company match and immediate vesting, Flexible Schedules, Discounted Stock Purchase Plans, Technical Upskilling, Education and Training Support, Parental Paid Leave, and much more.

Join us and make a difference in National Security!

The program is seeking an AI Engineer with a proven track record in software engineering and deep understanding of Artificial Intelligence/Machine Learning (AI/ML) techniques to support the exciting new AI-Assisted development mission. The AI engineer will work closely with mission stakeholders to capture requirements, aid in structured planning, and improve the certification process by introducing AI/ML tooling. Additionally, the engineer will use AI/ML to develop tools and techniques to include fine tuning Large Language Models (LLM) that assist in enhanced vulnerability identification and mitigation capabilities.

Position Responsibilities:

• Lead AI development across a 20+ person engineering team, supporting AI-enabled software modernization.
• Build AI/ML tools including LLM orchestration, RAG pipelines, automation workflows, and mission-focused applications.
• Collaborate with customers through technical meetings, architecture reviews, and briefings.
• Design AI system architectures, integration strategies, secure APIs, and user-facing tools.
• Integrate AI into engineering workflows for requirements analysis, code generation, testing, vulnerability scanning, and release processes.
• Configure AI agents, plugins, and automation frameworks to boost developer productivity.
• Develop documentation including user guides, API references, and operational playbooks.
• Support deployment and testing of AI-enabled systems in secure environments.
• Optimize AI models for performance, scalability, and mission reliability.
• Ensure security compliance in partnership with cybersecurity and Dev Sec Ops  teams.

Required Qualifications:

• Bachelor's Degree in System Engineering, Computer Science, Information Systems, Engineering Science, Engineering Management, or a related field and at least 8+ years of related experience. Additional experience may be substituted for a Degree.
• AI-Enabled SDLC Architecture:
  Experience designing or implementing AI-enabled software development lifecycle (SDLC) architectures and developer productivity platforms. Familiarity with internal developer portals, CI/CD automation, software delivery tooling, and secure development workflows. Understanding of how AI coding agents integrate across the full SDLC, including requirements analysis, system design, implementation, code review, automated testing, security scanning, release management, documentation generation, and operational support.
• AI Coding Agent
  
  Experience:
  
  Hands-on experience using, configuring, or extending AI-assisted development tools and coding agents such as Open Code, Claude Code, Git Hub Copilot, Cursor, Aider, Continue, Sourcegraph Cody, or similar agentic development environments. Understanding of how coding agents perform repository inspection, task planning, code modification, command execution, diagnostic interpretation, and iterative remediation workflows.
• Open Code Extensibility & Workflow Integration:
  Experience configuring or extending Open Code or similar agentic development platforms through custom agents, tools, plugins, hooks, and workflow integrations. Familiarity with integrating external services and automation pipelines using MCP servers, plugin frameworks, and event-driven customization mechanisms.
• Model Context Protocol (MCP) Expertise:
  Experience building, integrating, or securing Model Context Protocol (MCP) servers and clients. Understanding of MCP concepts including tools, resources, prompts, transports, authentication, authorization, permissions management, context isolation, and safe execution boundaries. Familiarity with enabling AI systems to securely interact with local file systems, APIs, databases, search services, and mission-specific workflows.
• Secure AI Engineering:
  Understanding of secure AI development practices, AI governance, model safety, prompt injection mitigation, data protection, and secure integration of LLM-powered systems within classified or restricted computing environments.
• Modern AI Infrastructure:
  Experience deploying and operating AI/ML services in containerized and orchestrated environments using Kubernetes, Docker, or cloud-native MLOps platforms. Familiarity with scalable inference architectures, GPU-enabled workloads, and automated model deployment pipelines.
• Must possess an active Secret clearance to be considered and would be…