AI Engineering Lead

CACI International•Washington, DC

1d•$103,800 - $218,100

About The Position

CACI is seeking an AI Engineering Lead to build and lead a multidisciplinary team delivering secure, production-ready AI applications and agent-based solutions for the United States Secret Service contract supporting the Department of Homeland Security. The role provides technical direction across the software development lifecycle, from discovery and solution design through development, testing, deployment, and sustainment. The team will use frameworks such as LangChain and Microsoft Agent Framework to develop AI capabilities integrated with enterprise data, applications, and workflows. This position combines hands-on technical leadership with team management. The lead translates mission and business needs into a prioritized roadmap, organizes work across AI engineers and supporting roles, and establishes engineering practices that support quality, security, scalability, and measurable outcomes. It requires sound judgment across the broader generative AI engineering stack, with an emphasis on delivering and operating secure AI systems. The role requires a bachelor's degree and 15 years of relevant experience, with stated education-and-experience equivalencies. The position requires the ability to obtain and maintain a public trust to begin employment. Travel may be required up to 10%.

Requirements

Must be able to obtain and maintain a public trust to begin employment.
Bachelor's degree plus 15 years of experience in artificial intelligence, machine learning, software engineering, or a related field; equivalencies considered: master's degree plus 12 years, 21 years with no degree, or an associate degree plus 17 years.
Demonstrated experience designing, building, and supporting production software that integrates AI models with enterprise applications, data, and APIs.
Experience developing AI or agent-based applications with LangChain, Microsoft Agent Framework, or comparable frameworks, and ability to explain framework design choices and tradeoffs.
Strong software engineering background in Python, APIs, microservices, automated testing, version control, and CI/CD.
Experience with generative AI patterns such as RAG, prompt and context design, tool calling, agent workflows, and model or application evaluation.
Experience deploying or operating AI-enabled applications in a cloud environment, preferably Microsoft Azure and Azure OpenAI or Azure AI services.
Experience establishing or applying security controls for AI applications, including identity and access management, authorization-aware data retrieval, privacy protections, and secure handling of sensitive information.
Experience leading multidisciplinary teams, setting priorities, reviewing technical work, coaching staff, and coordinating delivery with product, cybersecurity, data, and operations stakeholders.
Ability to communicate technical concepts, risks, options, and recommendations clearly to engineering teams and executive or mission stakeholders.

Nice To Haves

Experience leading AI initiatives in federal government, law enforcement, investigative, or national security environments, with understanding of sensitive data handling, ATO processes, and compliance requirements.
Hands-on experience with MLOps, model lifecycle management, AI governance frameworks, and production AI monitoring and observability tools.

Responsibilities

Lead, mentor, and coordinate a multidisciplinary team that may include AI and software engineers, data engineers, product or business analysts, UX specialists, and DevSecOps staff.
Provide technical direction for AI applications and agentic solutions, including architecture, design patterns, framework selection, integration, testing, deployment, and ongoing operations.
Guide development using industry-standard agentic frameworks.
Lead implementation of retrieval-augmented generation (RAG), tool and API integrations, agent workflows, and other capabilities connecting AI models to authorized enterprise data and business processes.
Translate mission objectives and user needs into technical approaches, product backlogs, user stories, acceptance criteria, and a sequenced delivery roadmap.
Establish engineering standards for Python and API development, reusable components, code review, automated testing, CI/CD, documentation, and secure software delivery.
Direct evaluation of AI system quality, including accuracy, relevance, grounding, safety, and performance, using test data and user feedback to guide improvements.
Establish monitoring and observability for application health, model behavior, latency, usage, and cost; lead troubleshooting and continuous improvement.
Coordinate with cybersecurity, data governance, infrastructure, cloud, product, and mission stakeholders to address access controls, privacy, security, and operational requirements.
Manage team priorities, staffing and skill alignment, delivery dependencies, risks, and progress reporting; communicate decisions and issues to technical and nontechnical stakeholders.
Promote responsible AI practices, including appropriate human review, traceable outputs, data protection, and clear handling of uncertainty and failure conditions.
Apply familiarity with AI safety frameworks such as the NIST AI Safety Framework to support implementation of alignment controls in AI LLMs and agents.
Contribute to AI safety and alignment policies and procedures to ensure ethical, controllable, and aligned AI systems and agents.