AI ENGINEER

**Job Description:** Type of Requisition: Regular

**Clearance Level Must Currently Possess:** Top Secret SCI + Polygraph

**Clearance Level Must Be Able To Obtain:** Top Secret SCI + Polygraph

**Public Trust/Other Required:** None

**Job Family:** Data Science and Data Engineering

**Skills:** Job Qualifications: Agentic AI, AI Agents, Amazon Web Services (AWS)

**Certifications:** None

**Experience:** 5 + years of related experience

**US Citizenship Required:** Yes

**Job Description:** AI ENGINEER

• Up to 3 Days per week telework! ***

Own your opportunity to turn data into measurable outcomes for our customers’ most complex challenges as an AI Engineer.

MEANINGFUL WORK AND PERSONAL IMPACT

As an AI Engineer, the work you’ll do at GDIT will be impactful to the mission of a mission-critical customer. You will play a crucial role in designing, prototyping, and integrating AI-enabled capabilities into an existing mission - critical tool.

• Support the application of existing large language models (LLMs) to real mission workflows rather than training new models.
• Collaborate with system and software engineers
• Build agentic solutions, systems of coordinated AI agents and prompts that automate tasks currently performed manually, streamline authoring and reporting workflows, and enhance the overall effectiveness of intelligence operations.

What You’ll Need To Succeed Bring your expertise and drive for innovation to GDIT. The AI Engineer must have:

• Education: Bachelor of Arts/Bachelor of Science
• Experience: 8+ years of related experience
• Security clearance level: TS/ SCI W/ Polygraph
• US citizenship required

**Responsibilities:**

• Agentic Solution Design & Development: Lead the design and development of AI-driven solutions from conception to deployment, building multi-agent workflows in which coordinated agents and prompts work together to produce outputs supporting reporting and dissemination. This includes prototyping, writing production-quality code, and maintaining deployed AI capabilities.
• LLM Integration: Integrate existing foundation models into the RAD architecture via APIs and orchestration frameworks, selecting the right model and approach for each task rather than building models from scratch.
• Prompt Engineering & Workflow Automation: Design, test, and refine prompts and agent instructions to reliably automate manual tasks, and decompose complex mission workflows into discrete steps an agent pipeline can execute.
• Evaluation & Reliability: Develop robust testing, evaluation, and validation strategies for AI-generated outputs, ensuring accuracy, consistency, and appropriate handling of edge cases before outputs reach analysts and reports.
• Collaboration & Communication: Serve as a key technical liaison, collaborating with cross-functional teams including system engineers, software developers, and domain experts. Effectively present and articulate recommended AI approaches, discussing the tradeoffs and implications of different implementations with both technical and non-technical stakeholders.
• Continuous Improvement: Stay current with the rapidly evolving LLM and agentic AI landscape (new models, orchestration frameworks, and tooling), continuously identifying practical opportunities to apply emerging capabilities to the system.

**Required Technical Skills:**

• Strong Python development skills, with experience writing production-quality, maintainable code
• Hands-on experience building applications on top of existing LLMs (e.g., via APIs for commercial or self-hosted models)
• Experience designing multi-agent or multi-step AI workflows, including prompt chaining, tool use, and agent orchestration (e.g., LangChain, LlamaIndex, or custom frameworks)
• Strong prompt engineering skills and experience evaluating/validating LLM outputs
• Experience integrating AI capabilities into existing software architectures (RESTful APIs, microservices)
• Experience with the Amazon Web Services (AWS) cloud computing platform

**Desired Technical Skills:**

• Experience with retrieval-augmented generation (RAG) pipelines and vector databases
• Familiarity with LLM observability and evaluation tooling
• Experience conducting exploratory data analysis on structured and unstructured datasets to prepare inputs for AI workflows
• Familiarity with MLOps/LLMOps practic…