Agentic AI Engineer

Booz Allen Hamilton•McLean, VA

1d•$112,800 - $257,000

About The Position

Agentic AI Engineer The Opportunity: AI is moving fast and the most consequential work is happening at the intersection of autonomous systems and real-world enterprise operations. As an Agentic AI Engineer, you'll be at the front edge of that wave, designing and building cutting-edge agentic AI solutions that don't just demo well, but deploy into production, scale across enterprise environments, and operate securely in some of the most demanding mission contexts imaginable. You'll lead and energize a development team that thrives on new tools and techniques, turning emerging capabilities like multi-agent orchestration, advanced RAG pipelines, and edge-deployed models, into solutions that change how our clients operate. If you're driven by the challenge of taking AI from prototype to production and you want to do it on problems that matter, this is your role. What You'll Work On: Embed agentic AI capabilities into existing enterprise systems and operational workflows, including API integration, process automation, and end-user adoption. Design and implement intelligent agent architectures that can reason, plan, and take actions. Develop and deploy collaborative multi-agent systems using Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols. Build advanced RAG pipelines integrating unstructured data with Knowledge Graphs (KG) to enhance reasoning accuracy and context retention. Fine-tune Small Language Models (SLMs) for specific domains and optimize them for edge device performance. Join us. The world can’t wait.

Requirements

6+ years of experience in software development
3+ years of experience working in cloud environments, including AWS and Azure, evaluating architectural tradeoffs, designing robust service-based software applications for scalable use, and using LangChain, LangGraph, AutoGen, PydanticAI, CrewAI, or LlamaIndex
Experience with MCP for tool integration, A2A for collaboration, RAG architecture and knowledge graphs, memory architectures for long-running agents such as episodic, semantic, and working memory patterns, and agent benchmarking frameworks such as GAIA, SWE-bench, or custom eval harnesses
Experience with tool or function calling patterns across multiple LLM providers
Experience fine-tuning LLMs or SLMs
Knowledge of modern software design patterns, including microservice design or edge computing
Ability to translate agentic AI capabilities into practical enterprise workflows, bridging the gap between technical implementation and business process adoption
Ability to adapt in a rapidly changing environment and navigate ambiguity
Ability to obtain a Secret clearance
Bachelor’s degree in CS or Computer Engineering

Nice To Haves

Experience deploying agentic systems in a production environment
Experience deploying agents on edge devices such as Android or local models
Experience integrating coding agents such as Cursor or Windsurf, into an efficient development pipeline with measured results
Experience with programming, including ML frameworks such as TensorFlow, PyTorch, llama.cpp, and vLLM
Experience engineering AI capabilities in on‑premises or multi‑classification environments
Possession of excellent verbal and written communication skills, for client engagements, client-facing project work, and business development

Responsibilities

Embed agentic AI capabilities into existing enterprise systems and operational workflows, including API integration, process automation, and end-user adoption.
Design and implement intelligent agent architectures that can reason, plan, and take actions.
Develop and deploy collaborative multi-agent systems using Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols.
Build advanced RAG pipelines integrating unstructured data with Knowledge Graphs (KG) to enhance reasoning accuracy and context retention.
Fine-tune Small Language Models (SLMs) for specific domains and optimize them for edge device performance.