AI Engineer

Booz Allen Hamilton•McLean, DC

7d•$99,000 - $225,000•Remote

About The Position

As an AI engineer, you’ll design and deliver production-grade AI systems. You’ll own RAG pipelines, evaluation systems, and performance optimization leveraging modern AI infrastructure and tooling to build scalable, observable, and efficient services. You'll work across the full AI application lifecycle from enterprise data ingestion and retrieval to prompt engineering, evaluation, deployment, observability, and production operations building secure, scalable AI solutions for mission-critical federal environments.

Requirements

3+ years of experience building production systems, including AI / ML applications
Experience building LLM systems using RAG frameworks such as LangChain and LlamaIndex, or custom pipelines and vector stores with Pinecone, Weaviate, FAISS, or OpenSearch Vector Search
Experience designing embedding pipelines, including document ingestion, chunking strategies, indexing, metadata filtering, retrieval optimization, and hybrid search, and designing APIs and services using FastAPI, Flask, or Node.js
Experience with cloud-native architectures, including AWS services such as Bedrock, SageMaker, EKS or ECS, S3, IAM, Lambda, CloudWatch, and Secrets Manager, and agent frameworks and orchestration patterns such as LangGraph, tool calling, and function calling APIs
Experience implementing observability, including logging and tracing with OpenTelemetry, Datadog, or CloudWatch, and metrics pipelines tracking latency, throughput, token usage, cache hit rate, and error rates
Experience optimizing performance using caching layers such as Redis, parallelization or async workflows, and chunking and retrieval tuning, and designing automated evaluation pipelines using benchmark datasets, LLM-as-a-judge techniques, regression testing, and human evaluation workflows
Knowledge of AI security concepts, including prompt injection, jailbreak resistance, data leakage prevention, guardrails, secure prompt design, and responsible AI practices
Ability to obtain a TS/SCI clearance
Bachelor's degree

Nice To Haves

Experience supporting DoD clients or other federal mission environments
Experience with high-performance inference systems such as vLLM, Ray Serve, or Triton Inference Server
Experience with semantic caching and embedding reuse strategies
Experience with guardrail frameworks such as Rebuff, Guardrails.ai, or custom filtering systems
Experience integrating AI applications with enterprise data platforms and knowledge repositories such as Databricks, Snowflake, OpenSearch, PostgreSQL, SharePoint, Confluence, or S3-based document stores
Experience deploying AI workloads into AWS GovCloud, IL4 or IL5 environments, or other secure cloud environments
Experience with event-driven architectures and messaging technologies such as Amazon SQS, Kafka, RabbitMQ, or Amazon EventBridge
Experience with Model Context Protocol (MCP) or modern AI agent interoperability standards

Responsibilities

Design and deliver production-grade AI systems.
Own RAG pipelines, evaluation systems, and performance optimization.
Leverage modern AI infrastructure and tooling to build scalable, observable, and efficient services.
Work across the full AI application lifecycle from enterprise data ingestion and retrieval to prompt engineering, evaluation, deployment, observability, and production operations.
Build secure, scalable AI solutions for mission-critical federal environments.
Support production AI services, including deployment, monitoring, incident response, debugging, and performance tuning.
Manage prompt templates, prompt versioning, model configurations, and structured outputs across development and production environments.