An Agentic AI Machine Learning Engineer develops production‑grade AI and ML capabilities for defense and intelligence programs, including work with large language models and intelligent agents. The role emphasizes deploying scalable solutions and operationalizing AI across mission environments.
Location
Washington, DC, onsite
Compensation
Salary range is USD 99,000 to 225,000 per year. Compensation is determined by factors such as location, education, knowledge, skills, and experience, along with contract-specific affordability and organizational considerations. This range represents the typical salary for this position and forms one component of total compensation.
Responsibilities
- Design and implement end-to-end AI systems that transform client operations, improve data accessibility, and optimize AI and ML workflows.
- Architect and expand AI/ML solutions to handle rapid data flows, scale with infrastructure based on usage, and adapt to evolving mission requirements.
- Ensure solutions account for the broader ecosystem and operating environment while planning for future functionality and enhancements.
- Advance expertise in software engineering, machine learning operations (MLOps), and deploying and integrating AI into diverse mission environments.
Requirements
- Bachelor's degree required; minimum three years of experience as a machine learning engineer delivering production-grade ML solutions, including work with LLMs, agents, or complex automation frameworks.
- Experience in data science or data research within professional or academic settings, with training or deploying models across multiple data modalities.
- Hands-on experience with cloud platforms such as AWS or Azure.
- Proficiency deploying and integrating production ML models using Docker and Kubernetes.
- Experience with Large Language Models, Deep Learning, and Reinforcement Learning, and with AI agent frameworks such as LangChain, LangGraph, PydanticAI, or llamaindex.
- Ability to connect agents to APIs, cloud platforms, or databases.
- Experience evaluating LLM performance and building observability layers for stakeholders using Grafana, Langfuse, LangSmith, or Phoenix.
- Experience assessing architectural tradeoffs and designing robust service-based software for scalable use.
- Ability to obtain a Secret security clearance.
Technologies
- AWS
- Azure
- Docker
- Kubernetes
- LangChain
- LangGraph
- PydanticAI
- llamaindex
- Grafana
- Langfuse
- LangSmith
- Phoenix
- TensorFlow
- PyTorch
- llama.cpp
- vLLM
- Kafka
- Red Panda
- Confluent
Benefits
- Health, life, disability, financial, and retirement benefits
- Paid leave
- Professional development
- Tuition assistance
- Work-life programs
- Dependent care
- Recognition awards program
Clearance
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information.
Identity Verification
As part of the hiring process, identity verification using advanced biometrics and artificial intelligence may be employed to ensure authenticity and prevent fraud. Interviews may require on‑camera participation, and a photo may be captured to verify identity.
Candidate AI Usage Policy
AI is part of Booz Allen's daily operations, and the organization is committed to responsible and ethical use of AI tools. Use of AI or other tools to assist with interview responses is prohibited unless explicitly permitted.
Work Model
Onsite work is conducted primarily at Booz Allen offices or customer facilities. Hybrid arrangements require frequent in‑person work at Booz Allen facilities with potential visits to customer sites. Remote work may be possible for certain tasks, but in-person participation could be required at times to meet role needs.
Commitment to Non-Discrimination
All qualified applicants will receive consideration for employment without regard to disability, veteran status, or any other status protected by applicable laws at the federal, state, local, or international level.
