Agentic AI Machine Learning Engineer

Booz Allen Hamilton

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Washington, DCMcLean, VAArlington, VAAlexandria, VAAnnapolis Junction, MD
Salary: $99,000–$225,000 / yr
Posted: 2 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $202k

This role $162k

$82k most similar roles pay here $259k

This role pays less than 75% of similar roles. Most pay $162,000–$241,750 — the shaded band above. At the midpoint, this role pays about $162k versus about $202k for comparable roles.

Based on 240 similar postings.

Employer

About Booz Allen Hamilton

Booz Allen Hamilton is a management and technology consulting firm that provides analytics, digital, engineering, and cybersecurity solutions primarily to U.S. government agencies and commercial clients. Industry: Management & Technology Consulting

Booz Allen Hamilton currently has 793 open roles on FindRole.

Listed pay typically runs $86,800–$198,000 across 768 roles with salary data.

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At a glance

TL;DR · Agentic AI Machine Learning Engineer

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As an Agentic AI Machine Learning Engineer, you will join the team to design, create, and implement complete artificial intelligence systems that transform client operations and improve data accessibility. You will build production-grade machine learning solutions, focusing on large language models, agents, and complex automation frameworks within the defense and intelligence sectors. Your daily work involves developing scalable service-based software applications, managing MLOps workflows, and integrating models into diverse mission environments. The role requires proficiency in Python and frameworks like TensorFlow or PyTorch, along with experience in Docker, Kubernetes, and cloud platforms such as AWS and Azure. You will utilize tools like LangChain, LangGraph, PydanticAI, or LlamaIndex to connect agents to APIs and databases while using Grafana or LangSmith for performance evaluation. This role addresses critical challenges in data accessibility and operationalizing AI systems.

Skills

LLM MLOps LangChain LangGraph PydanticAI LlamaIndex AWS Azure Docker Kubernetes TensorFlow PyTorch llama.cpp vLLM Kafka Red Panda Confluent Grafana Langfuse LangSmith Phoenix NLP Computer Vision

What you'll do

Design and implement complete AI systems to improve data accessibility and optimize machine learning operations.
Build production-grade ML solutions involving Large Language Models (LLMs), agents, and complex automation frameworks.
Deploy and integrate ML models into various mission environments using Docker and Kubernetes.
Connect AI agents to external APIs, cloud platforms, and databases.
Evaluate LLM performance and build observation layers for stakeholders using tools like Langfuse or LangSmith.
Architect scalable, service-based software applications that handle fast-moving data and evolving requirements.
Manage the full MLOps lifecycle including model training, deployment, and integration into production environments.

What we're looking for

Master's degree (preferred).
3+ years of experience as an ML engineer building production-grade solutions with LLMs, agents, or complex automation frameworks.
3+ years of experience in data science or research training and deploying models across multiple modalities.
3+ years of experience working in cloud environments including AWS and Azure.
2+ years of experience deploying and integrating production-grade ML models using Docker and Kubernetes.
Experience with LLM, Deep Learning, Reinforcement Learning, and frameworks like LangChain, LangGraph, PydanticAI, or LlamaIndex.
Ability to obtain a Secret clearance (or current Secret clearance preferred).