Jobs
Benefits & Perks
•Healthcare
•Life Insurance
•Disability Insurance
•401(k)
•Paid Leave
•Professional Development
•Tuition Assistance
•Work Life Programs
•Dependent Care
•Healthcare
•401k
•Learning
Required Skills
Machine learning
LLMs
Prompt engineering
Databricks
MLFlow
AI engineering
Generative AI/ML Engineer
The Opportunity:
As an experienced artificial intelligence and machine learning engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to conduct statistical analyses on business processes using ML techniques, including through the integration of large language models, makes you an integral part of delivering a customer-focused solution. We need your technical knowledge and desire to problem-solve to support new model development. As a machine learning engineer on our health team, you’ll train, test, deploy, and maintain models that learn from data.
In this role, you’ll own and define the direction of mission-critical solutions by applying best-fit ML algorithms and technologies. You’ll be part of a large community of machine learning engineers across the company and collaborate with data scientists, software developers, and cloud data engineers to deliver world-class solutions to invent and generate models for automation and decision making. Your skills and extensive technical expertise will guide clients as they navigate the landscape of ML algorithms, tools, and frameworks.
Join us. The world can’t wait.
You Have:
-
4+ years of experience with machine learning engineering on software engineering or data engineering projects, including for classification, regression, and decision-making
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Experience building AI-enabled features or prototypes using LLMs, embeddings, or 3rd party APIs, including Open
AI or Anthropic:
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Experience with basic prompt engineering concepts and using LLM SDKs or frameworks, including Lang Chain or Llama Index
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Experience using Databricks and MLFlow to efficiently manage data and ML workflows and optimizing performance and scalability for model experimentation, governance, or inferencing
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Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements
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Bachelor's degree
Nice If You Have:
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Experience building production-grade ML solutions, including work involving LLMs, agents, or complex automation frameworks
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Experience with cloud platforms, including AWS, Azure, or GCP, and their AI or ML services
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Experience developing back-end services and building or consuming RESTful APIs
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Experience with Python or Py Spark coding
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Experience with container technology, including Docker or Kubernetes
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Experience with software configuration management using Git
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Knowledge of how to use Model Context Protocol to integrate agents into AI/ML-supported automation systems
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Ability to work with limited guidance and clear direction
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Possession of excellent verbal and written communication skills to communicate with peers and clients
Vetting:Applicants selected will be subject to a government investigation and may need to meet eligibility requirements of the U.S. government client.
Compensation:
At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.
Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $77,600.00 to $176,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen’s total compensation package for employees. This posting will close within 90 days from the Posting Date.
Identity Statement
As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Work Model Our people-first culture prioritizes the benefits of flexibility and collaboration, whether that happens in person or remotely.
- If this position is listed as remote or hybrid, you’ll periodically work from a Booz Allen or client site facility.
- If this position is listed as onsite, you’ll work with colleagues and clients in person, as needed for the specific role.
Commitment to Non-Discrimination:
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.
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About Booz Allen Hamilton

Booz Allen Hamilton
PublicSpecializing in digital transformation and artificial intelligence.
10,001+
Employees
McLean
Headquarters
Reviews
3.5
5 reviews
Work Life Balance
2.5
Compensation
3.0
Culture
2.8
Career
2.5
Management
2.0
35%
Recommend to a Friend
Pros
Great reputation in federal sector
Hybrid work options available
Established teams and processes
Cons
Poor hiring and recruitment process
Job security concerns with contract-based work
Work-life balance issues
Salary Ranges
34 data points
Junior/L3
L2
L3
L4
L5
L6
Mid/L4
Senior/L5
Junior/L3 · Data Engineer
1 reports
$149,500
total / year
Base
$115,000
Stock
-
Bonus
-
$149,500
$149,500
Interview Experience
7 interviews
Difficulty
2.7
/ 5
Duration
21-35 weeks
Offer Rate
43%
Experience
Positive 29%
Neutral 28%
Negative 43%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Interview
4
Hiring Manager Interview
5
Team Interview
6
Offer
Common Questions
Technical Knowledge
Behavioral/STAR
Past Experience
System Design
Culture Fit
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