AI Engineer

CACI•Denver, CO

About The Position

CACI is seeking an AI Engineer to join our AI Center of Excellence, part of a fast-moving CTO organization driving the integration and scaling of AI across CACI's mission, program, and business operations. The AI Engineer will deliver rapid, high-impact AI solutions across diverse programs. This is a rapid, prototype development role — you'll support different programs in 1-2 month rotational engagements, deploying working AI applications, establishing best practices, and transferring knowledge to make teams operationally independent. You'll work hands-on with cutting-edge GenAI technologies—building RAG pipelines, deploying conversational AI platforms, implementing multi-agent systems—while leveraging our solution catalog of battle-tested templates. Between engagements, you'll contribute your field learnings back to the catalog, creating reusable patterns that accelerate AI adoption across CACI. This role is ideal for engineers who thrive on variety over deep ownership, value making others successful, and want to shape how the Federal Government adopts AI across diverse mission areas.

Requirements

3–5 years building production applications with Python/JavaScript, Git workflows, and modern development practices.
Practical experience with LLM‑powered apps, agent patterns, RAG, prompt engineering, vector databases, and observability concepts; hands‑on experimentation preferred.
Monitor AI performance (latency, cost, quality), address common failure modes, and apply responsible AI practices such as bias detection and guardrails.
Strong background designing, implementing, and troubleshooting RESTful and event‑driven integrations.
Experience with AWS/Azure/GCP, containerization, CI/CD, IaC concepts, and secure API/key management.
Understanding of basic ML concepts and how they apply to LLM systems.
Proven ability to deliver solutions quickly in unfamiliar environments with evolving requirements.
Strong communication skills and ability to create clear documentation and teach complex AI concepts.
Ability to make trade‑offs under pressure, prioritize working solutions, and leverage reusable templates.
Active user of modern AI tools; stays current through experimentation and community engagement.
Experience with GitLab, Jira, and iterative delivery.
Ability to obtain and maintain a up to a Top Secret clearance.

Nice To Haves

Experience deploying agentic AI systems, using observability tools, vector databases, guardrails, embeddings, and structured outputs.
AWS (Bedrock/GovCloud), Azure OpenAI, Kubernetes, Terraform, and CI/CD pipeline experience.
Proficiency in JS/TS/Python for front‑end/back‑end development and modern frameworks like React or FastAPI.
Experience leading client engagements, context‑switching across projects, and delivering strong knowledge transfer.
Familiarity with DoD/federal missions, security requirements, and compliance frameworks (ATO, NIST).
History of open‑source work, technical writing, conference speaking, or similar community involvement.
Security+, AWS certifications, or other relevant technical credentials.

Responsibilities

Deliver production-ready AI solutions in 1–2‑month rotations, including RAG pipelines, conversational platforms, and multi‑agent systems tailored to each program’s mission and tech stack.
Build and customize AI solutions using vector databases, orchestration frameworks, and managed AI services while implementing observability, security, and cost controls.
Integrate LLM APIs and AI services into existing workflows; apply responsible AI guardrails; configure monitoring/alerting; and resolve integration issues across cloud and on‑prem environments.
Lead hands‑on training, create documentation, and pair‑program with teams to ensure they can independently operate and evolve AI applications.
Improve existing templates, create reusable patterns, and document new techniques based on field experience.
Confirm teams reach full operational independence through structured handoff and validation processes.
Explore emerging GenAI tools, evaluate federal use‑case applicability, and share insights through demos and documentation.