Artificial Intelligence Engineer

Booz Allen Hamilton•San Antonio, TX

About The Position

As an AI Engineer, you will bring advanced AI capabilities into production systems by integrating models, inference endpoints, agent-orchestration frameworks, and intelligent workflows into complex, distributed software environments. You will apply strong software engineering principles to ensure these AI features operate reliably, securely, and efficiently at scale. You will help evolve traditional LLM-driven solutions into goal-oriented, multi-agent architectures 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 strategies, refine integration workflows, collaborate with cross-functional engineering teams, and develop scalable AI capabilities that translate cutting-edge concepts into operational, mission-ready solutions.

Requirements

At least 3 years of experience developing and maintaining production-grade software systems in Python.
Experience designing and implementing AI agent architectures using LangChain, LangGraph, AutoGen, MCP, or A2A.
Experience building RAG solutions with Knowledge Graphs and fine-tuning SLMs using ONNX, GGML, or Ollama.
Experience integrating LLMs, external APIs, and distributed services into production with optimized inference workflows.
Experience developing AI evaluation frameworks, integration tests, and production validation using ReAct patterns 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 Computer Science, Engineering, or Data Science.
DoD 8140 Level II Certification such as Security+, CySA+, or CISSP.

Nice To Haves

Experience supporting DoD clients or other federal mission environments.
Experience with advanced agentic patterns, including prompt caching, guardrails, agent memory, Model Context Protocol (MCP), and agent deployment strategies.
Experience developing end-to-end chatbot or agent-to-agent (A2A) systems.
Experience with enterprise data and machine learning platforms such as Databricks or Snowflake.
Experience building or managing large-scale data processing pipelines.
Master’s degree in a related field.

Responsibilities

Integrate AI models, inference endpoints, agent-orchestration frameworks, and intelligent workflows into complex distributed software environments.
Apply software engineering principles to ensure AI features operate reliably, securely, and efficiently at scale.
Help evolve LLM-driven solutions into goal-oriented, multi-agent architectures using agent-orchestration frameworks, RAG, Knowledge Graphs, and fine-tuned SLMs.
Contribute to AI evaluation and monitoring strategies and refine integration workflows.
Collaborate with cross-functional engineering teams to develop scalable, mission-ready AI capabilities.