AI Engineer with TS/SCI Clearance & Full Scope Polygraph - Hybrid role in Annapolis Junction

Booker DiMaio; Maryland; Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work model

On-site in Annapolis Junction, MD

Job summary

Booker DiMaio in Annapolis Junction, MD, seeks an AI Engineer with an active TS/SCI clearance for a hybrid role (up to 3 days remote, 2 days onsite). You will design, prototype, and integrate AI-enabled capabilities into existing software, focusing on applying large language models to real mission workflows rather than training new models.

You will build multi-agent workflows, integrate foundation models via APIs, engineer prompts, and ensure reliable outputs for reporting.

Qualifications

• Strong Python development with production-quality code.
• Experience building apps on top of existing LLMs via APIs.
• Designing multi-agent or multi-step AI workflows with prompts.
• Experience integrating AI into software architectures (RESTful APIs, microservices).
• Experience with AWS cloud computing platform.

Responsibilities

• Lead design and development of AI-driven solutions and multi-agent workflows.
• Integrate foundation models into existing software via APIs and orchestration frameworks.
• Design, test, and refine prompts to automate tasks and decompose workflows.
• Develop robust evaluation strategies for AI outputs and edge cases.
• Collaborate with system engineers, software developers, and domain experts.

Skills

Python development

LLM integration

Multi-agent workflows

Prompt engineering

API/microservices

AWS cloud

Tools

LangChain

LlamaIndex

Job description

An active Top Secret/SCI Clearance with a Full Scope Polygraph is required.

This is a hybrid schedule with up to 3 days/week remote and 2 days/week onsite in Annapolis Junction, MD.

We have an immediate need for an Artificial Intelligence (AI) Engineer to support the development and sustainment of mission-focused AI capabilities. The AI Engineer will work closely with system and software engineers to design, prototype, and integrate AI-enabled capabilities into existing software architectures. This role centers on applying existing large language models (LLMs) to real mission workflows rather than training new models. The AI Engineer will 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.

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 existing software architectures 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 practices for deploying and monitoring AI capabilities at scale
• Familiarity with domain knowledge surrounding government agency reporting and dissemination policies