AI Engineer

Booz Allen Hamilton•Washington, DC

8d•$77,600 - $176,000•Remote

About The Position

Join a hands-on AI engineering team building and deploying LLM-powered applications. In this role, you’ll implement core components of AI systems, including prompting, retrieval pipelines, and API integrations, while gaining experience with the tooling and infrastructure required to run AI workloads in production. You'll work across the full AI application lifecycle from data ingestion and retrieval to prompt engineering, evaluation, deployment, and production operations building secure, mission-focused AI solutions for federal clients.

Requirements

1+ years of experience in software engineering, data engineering, or applied AI/ML
Experience building applications in Python using frameworks such as FastAPI or Flask, and building and integrating applications using LLM APIs such as OpenAI, AWS Bedrock, or Azure OpenAI
Experience implementing Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embeddings, retrieval workflows, and prompt augmentation
Experience with vector databases such as FAISS, Pinecone, Weaviate, or OpenSearch Vector Search, and cloud environments, including AWS services such as S3, Lambda, EC2, IAM, and CloudWatch
Experience using Git, basic CI/CD pipelines, and containerization with Docker
Experience designing or consuming REST APIs and working with structured outputs such as JSON schemas and function or tool calling
Knowledge of prompt engineering, prompt evaluation, and common LLM failure modes, including hallucinations, prompt injection, data leakage, jailbreak attempts, and grounding techniques
Knowledge of prompt evaluation techniques and response quality testing
Ability to obtain a TS/SCI clearance
Bachelor's degree

Nice To Haves

Experience with orchestration frameworks such as LangChain or LlamaIndex
Experience with embeddings via OpenAI, Cohere, or SentenceTransformers
Experience with monitoring and logging tools such as CloudWatch or Datadog
Experience deploying services using Docker Compose or Kubernetes
Experience with structured logging and debugging distributed API failures
Experience with AWS Secrets Manager or AWS Systems Manager Parameter Store
Experience with asynchronous Python programming or background processing
Experience with Model Context Protocol (MCP) or modern AI tool integration standards

Responsibilities

Implement core components of AI systems, including prompting, retrieval pipelines, and API integrations.
Gain experience with the tooling and infrastructure required to run AI workloads in production.
Work across the full AI application lifecycle from data ingestion and retrieval to prompt engineering, evaluation, deployment, and production operations.
Build secure, mission-focused AI solutions for federal clients.