Agentic AI Engineer
About The Position
As an Agentic AI Engineer at Booz Allen, you will design and build agentic AI solutions that move beyond prototypes into secure, scalable production use across enterprise environments. You will lead and energize a development team, applying emerging capabilities such as multi-agent orchestration, advanced retrieval-augmented generation (RAG), and edge-deployed models to client mission needs. The role focuses on embedding agentic AI into enterprise systems and operational workflows, building intelligent and collaborative agent architectures, enhancing reasoning with knowledge graphs, and fine-tuning small language models for domain-specific and edge-device use. You will help translate technical capabilities into practical workflows and support enterprise adoption.
Requirements
- At least 6 years of software development experience.
- At least 3 years of experience working in cloud environments, including AWS and Azure, evaluating architectural tradeoffs, designing robust and scalable service-based software applications, and using one or more of 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 (including episodic, semantic, and working memory patterns), and agent benchmarking frameworks such as GAIA, SWE-bench, or custom evaluation 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 and bridge technical implementation with business process adoption.
- Ability to adapt in a rapidly changing environment and navigate ambiguity.
- Ability to obtain a Secret clearance.
- Bachelor’s degree in computer science or computer engineering.
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, including with ML frameworks such as TensorFlow, PyTorch, llama.cpp, and vLLM.
- Experience engineering AI capabilities in on-premises or multi-classification environments.
- 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 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 that integrate unstructured data with knowledge graphs to improve reasoning accuracy and context retention.
- Fine-tune small language models for specific domains and optimize them for edge-device performance.
- Lead and energize a development team working with emerging tools and techniques.
Benefits
- Health, life, and disability benefits.
- Financial and retirement benefits.
- Paid leave.
- Professional development.
- Tuition assistance.
- Work-life programs.
- Dependent care.
- Recognition awards program.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
