Agentic AI Engineer

Booz Allen Hamilton•Annapolis Junction, VA

18d

About The Position

Booz Allen is seeking an Agentic AI Engineer to build autonomous, goal-oriented AI systems and help shift its AI strategy from passive LLM chatbots to proactive, multi-agent orchestration. The role involves designing agent architectures that reason, plan, and take action; developing collaborative multi-agent systems; and building RAG solutions that combine unstructured data with Knowledge Graphs. The engineer will also fine-tune and optimize Small Language Models for edge deployment and create evaluation frameworks to assess reliability, safety, and performance as systems move from prototype to production. The position works with agent orchestration frameworks and protocols, cloud services, model fine-tuning methods, and observability tools. The successful candidate will contribute to robust, scalable software applications and adapt effectively in a rapidly changing environment. Top Secret clearance is required. The posting describes Booz Allen’s benefits and compensation, and notes that employees may work remotely, in a hybrid arrangement, or onsite depending on how the position is listed; it does not specify this position’s workplace type.

Requirements

At least 2 years of software development experience.
At least 2 years of experience as an ML engineer building production-grade ML solutions using tools such as Docker or Kubernetes, including work with GenAI, LLMs, deep learning, reinforcement learning, AI agents, agentic workflows, or complex automation frameworks.
At least 2 years of experience with LangChain, LangGraph, AutoGen, PydanticAI, CrewAI, or LlamaIndex.
At least 2 years of experience working in cloud environments, including AWS and Azure, evaluating architectural tradeoffs, and designing robust service-based software applications for scalable use.
Experience with MCP for tool integration and A2A for agent-to-agent collaboration, and with RAG architecture and Knowledge Graphs, including Neo4j or NebulaGraph.
Experience fine-tuning LLMs or SLMs using Hugging Face, PEFT, or LoRA.
Experience evaluating LLM performance and behavior and building stakeholder observation layers using tools such as Grafana, Langfuse, LangSmith, or Phoenix.
Knowledge of modern software design patterns, including microservice design or edge computing.
Ability to adapt in a rapidly changing environment and navigate ambiguity.
Top Secret clearance.
Bachelor’s degree.

Nice To Haves

Experience deploying agentic systems in a production environment.
Experience deploying agents on edge devices such as Android or using local models.
Experience integrating coding agents such as Cursor or Windsurf into an efficient development pipeline with measured results.
Programming experience with ML frameworks or tools such as TensorFlow, PyTorch, llama.cpp, and vLLM.
Experience engineering AI capabilities in on-premise or multi-classification environments.
Excellent verbal and written communication skills for client engagements, client-facing project work, and business development.
Master’s degree in a computer science or AI field preferred; doctorate in computer science or statistics is a plus.

Responsibilities

Design and implement intelligent agent architectures that reason, plan, and take actions using LangChain, LangGraph, and AutoGen.
Develop and deploy multi-agent systems using MCP and A2A protocols for communication, tool use, and collaborative task solving.
Build advanced RAG pipelines that integrate unstructured data with Knowledge Graphs to improve reasoning accuracy and context retention.
Fine-tune SLMs for specific domains and optimize them for edge-device performance, including with ONNX, GGML, or Ollama.
Develop frameworks to evaluate agent reliability, safety, and performance as systems move from prototype to production, including ReAct loops and human-in-the-loop approaches.