AI Engineer

Booz Allen Hamilton•San Antonio, TX

12h•Remote

About The Position

As an AI Engineer, you will integrate AI-enabled capabilities into existing software systems by connecting models, inference endpoints, agent orchestration components, and intelligent autonomous workflows to production applications. You will implement AI features using established software engineering patterns, ensuring these capabilities operate reliably, securely, and efficiently within distributed systems. In this role, you will help evolve AI solutions from traditional LLM-based interactions to autonomous, goal-oriented, and multi-agent systems by leveraging agent orchestration frameworks, Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and fine-tuned Small Language Models (SLMs). You will contribute to evaluation and monitoring approaches, refine integration workflows, collaborate with cross-functional teams, and develop scalable AI functionality that brings intelligent, production-ready AI solutions from concept into real-world environments.

Requirements

3+ years of experience developing and maintaining production software systems in Python
Experience designing and implementing intelligent AI agent architectures using LangChain, LangGraph, AutoGen, MCP, and A2A
Experience building RAG solutions with KGs and fine-tuning SLMs using ONNX, GGML, or Ollama
Experience integrating LLMs, external APIs, and distributed services into production environments with optimized inference workflows
Experience developing AI evaluation frameworks, integration tests, and production validation using ReAct and human-in-the-loop methodologies
Knowledge of secure HTTP APIs, OAuth2, JWT, API Keys, containerization, and CI/CD pipelines
Ability to travel up to 25% of the time
TS/SCI clearance
Bachelor's degree in CS, Engineering, or Data Science
DoD 8140 baseline Level II Certification such as Security+, CySA+, CISSP, or DoD Cyber Workforce Framework Certification

Nice To Haves

Experience supporting DoW clients or other federal mission environments
Experience with advanced agentic patterns, including prompt caching, guardrails, agentic memory, Model Context Protocol (MCP), and agentic deployment strategies
Experience with end-to-end chatbot or agent to agent (A2A) development
Experience with enterprise data and ML platforms such as Databricks or Snowflake
Experience creating and managing large data processing pipelines
Master’s degree

Responsibilities

Integrate AI-enabled capabilities into existing software systems by connecting models, inference endpoints, agent orchestration components, and intelligent autonomous workflows to production applications.
Implement AI features using established software engineering patterns, ensuring these capabilities operate reliably, securely, and efficiently within distributed systems.
Evolve AI solutions from traditional LLM-based interactions to autonomous, goal-oriented, and multi-agent systems by leveraging agent orchestration frameworks, Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and fine-tuned Small Language Models (SLMs).
Contribute to evaluation and monitoring approaches.
Refine integration workflows.
Collaborate with cross-functional teams.
Develop scalable AI functionality that brings intelligent, production-ready AI solutions from concept into real-world environments.