This role pays less than
93%
of similar roles. Most pay
$162,000–$246,150
— the shaded band above.
At the midpoint, this role pays about
$127k
versus about
$204k
for comparable roles.
Based on 240 similar postings.
Employer
About Booz Allen Hamilton
Booz Allen Hamilton is a management and technology consulting firm that provides analytics, digital, engineering, and cybersecurity solutions primarily to U.S. government agencies and commercial clients. Industry: Management & Technology Consulting
Booz Allen Hamilton currently has
809 open roles
on FindRole.
Listed pay typically runs
$86,800–$198,000
across 788 roles with salary data.
The Agentic AI Engineer joins the team to transition AI strategy from passive chatbots toward proactive, multi-agent orchestrations. You will design and implement intelligent agent architectures that reason, plan, and take actions using LangChain, LangGraph, and AutoGen. Key responsibilities include developing multi-agent systems via Model Context Protocol and Agent-to-Agent protocols, building advanced RAG pipelines integrated with Knowledge Graphs like Neo4j or NebulaGraph, and fine-tuning Small Language Models for edge deployment using ONNX, GGML, or Ollama. You will also build evaluation frameworks to test reliability and safety through ReAct loops. Required skills include experience with PydanticAI, CrewAI, LlamaIndex, and Hugging Face tools like PEFT or LoRA. Technical proficiency in Docker, Kubernetes, AWS, Azure, and Python is essential for building robust service-based applications in complex environments requiring a TS/SCI clearance.
Design and implement intelligent agent architectures using LangChain, LangGraph, and AutoGen.
Develop multi-agent systems utilizing Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols.
Build advanced RAG pipelines integrating unstructured data with Knowledge Graphs to improve reasoning.
Fine-tune Small Language Models (SLMs) for specific domains and optimize them for edge device performance.
Develop evaluation frameworks to test agent reliability, safety, and performance in production environments.
Implement ReAct loops and human-in-the-loop systems to ensure reliable autonomous actions.
Build observation layers using tools like Langfuse or LangSmith to monitor system behavior.
What we're looking for
TS/SCI clearance with polygraph.
2+ years of experience in software development.
2+ years of experience as an ML engineer building production-grade solutions using Docker, Kubernetes, and GenAI frameworks.
2+ years of experience with LangChain, LangGraph, AutoGen, PydanticAI, CrewAI, or LlamaIndex.
2+ years of experience working in cloud environments including AWS and Azure to design scalable service-based applications.
Experience with MCP, A2A protocols, RAG architecture, Knowledge Graphs (Neo4j/NebulaGraph), and fine-tuning LLMs/SLMs using Hugging Face, PEFT, or LoRA.
Knowledge of modern software design patterns like microservice design or edge computing; advanced degrees in CS or AI are preferred.