Interested in this AI/ML Engineer role at Booz Allen Hamilton?
Apply Now →Skills & Technologies
About This Role
The Opportunity:
As organizations adopt AI systems that can reason, use tools, and take action, they need engineers who understand both how these systems work and how to secure them. We’re looking for an AI / ML engineer who can combine hands-on development, security expertise, and architectural thinking to help our customers deploy agentic AI securely and responsibly.
As a security-focused AI / ML Engineer on our team, you’ll design, build, and evaluate secure AI applications and agentic workflows. You’ll examine how models, agent orchestration, tools, data sources, and enterprise infrastructure interact, identify security risks, and develop practical controls throughout the system lifecycle. You’ll turn emerging research and complex technical requirements into working prototypes, reusable architecture patterns, and clear technical guidance.
You’ll collaborate with AI engineers, cybersecurity spe cia lists, architects, and mission stakeholders to develop solutions that balance capability, security, and operational needs. Your work will help technology leaders and implementation teams understand how to move from AI experimentation to secure, governed deployment.
What You’ll Work On:
- Design and validate secure architectures for generative AI applications, AI agents, and multi-agent systems, including model access, orchestration, tool execution, memory, and retrieval augmented generation.
- Develop prototypes and reference implementations to demonstrate security controls and evaluate architectural tradeoffs.
- Assess AI systems for risks such as prompt injection, sensitive data exposure, excessive permissions, u nsa fe tool execution, and compromised integrations.
- Design and implement controls for agent identity, least privilege access, execution isolation, tool authorization, data protection, and human approval of consequential actions.
- Develop architecture diagrams, data flow diagrams, t rus t boundary models, and technical documentation that explain system behavior, control placement, and implementation requirements.
- Establish evaluation and monitoring approaches for agent behavior, security, reliability, and policy compliance, including t race ability across model interactions and tool calls.
- Translate security and governance requirements into engineering practices, including testing, version control, change management, audit trails, and rollback procedures.
- Evaluate emerging models, agent frameworks, integration protocols, and security tools, and communicate findings and recommendations to technical and non-technical audiences.
- Provide day-to-day technical leadership to engineers.
- Conduct design reviews, guide architectural decisions, and raise the overall technical bar of the team.
Join us. The world can’t wait.
You Have:
- 8+ years of experience in sof tware engineering or AI / ML engineering, including developing and deploying AI / ML systems
- Experience developing in Python and applying sof tware engineering practices, including Git, automated testing, code review, and dependency management
- Experience building applications using large language models, including tool use, retrieval augmented generation, and agent orchestration
- Experience designing or evaluating system architectures involving APIs, data pipelines, model services, and enterprise integrations
- Experience applying cybersecurity principles to sof tware or AI systems, including identity and access management, t rus t boun dari es, secure data handling, and threat modeling
- Experience with traditional ML approaches and algorithms and their applications to cybersecurity
- Knowledge of NIST security and risk management frameworks, including SP 800-53 and AI Risk Management Framework, and Zero T rus t Architecture principles, including identity-based access, least privilege, and continuous verification
- Knowledge of AI-specific security risks and approaches for evaluating and mitigating vulnerabilities in generative AI and agentic systems
- Ability to obtain a Secret clearance
- Bachelor’s degree in CS, Mathematics, Engineering, or Cybersecurity
Nice If You Have:
- Experience developing agentic AI applications using frameworks or platforms such as Strands Agents, LangGraph, Amazon Bedrock, GraphRAG, or comparable technologies, including integrating agents with workflow orchestration tools such as Temporal
- Experience securing tool integrations using Model Context Protocol or other agent communication and integration mechanisms
- Experience conducting AI red teaming, adversarial testing, or automated security evaluations of models and agentic workflows
- Experience implementing AI gateways, policy enforcement, execution sandboxes, agent observability, or human-in-the-loop controls
- Experience deploying AI applications in cloud or on-premises environments using containers, CI / CD pipelines, and Infrastructure-as-Code
- Experience with model training, fine-tuning, evaluation, or serving using frameworks such as PyTorch or comparable technologies
- Experience with distributed computing, data engineering, feature pipelines, or relational and vector databases
- Experience applying NIST guidance and Zero T rus t Architecture principles to AI systems, and translating security requirements into architectural controls, implementation patterns, and assessment evidence
- TS/SCI clearance
Clearance:
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information.
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 $99,000.00 to $225,000.00 (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 hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. 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.
Candidate AI Usage Policy
AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided .
Work Model
Our people-first culture prioritizes the benefits of collaboration. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.
- Remote : If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.
- Hybrid : If this position is listed as hybrid, you will be expected to work from a Booz Allen or customer facility, in alignment with your leadership's expectations and the needs of the role.
- Onsite : If this position is listed as onsite, work will primarily be performed full-time at a customer facility, where employees will collaborate directly with colleagues and customers as required by the 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.
Salary Context
This $99K-$225K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Booz Allen Hamilton, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($162K) sits 24% below the category median. Disclosed range: $99K to $225K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Booz Allen Hamilton AI Hiring
Booz Allen Hamilton has 43 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, MLOps Engineer. Positions span Alexandria, VA, US, McLean, VA, US, San Diego, CA, US. Compensation range: $126K - $292K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.