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About This Role
Cybersecurity AI/ML EngineerThe Opportunity:
As a Cybersecurity AI/ML Engineer, you will operate as a hands\-on technical contributor and engineering leader responsible for building, scaling, and operationalizing AI/ML systems that power Booz Allen's Cyber Operations teams. This role emphasizes production engineering and platform delivery, turning models, security telemetry, and analyst workflows into reliable, low\-latency, observable services and pipelines that measurably improve prevention, detection, response, and recovery outcomes.
You will bridge ML engineering and security operations by translating models, threat models, and analyst needs into production\-grade data and feature pipelines, training systems, inference services, and monitoring frameworks deployed across cloud, network, endpoint, identity, and application telemetry domains. You will originate, facilitate, and lead cross\-functional efforts to mature AI\-enabled cybersecurity capabilities, including real\-time detection inference at scale, alert triage automation, LLM and agentic analyst tooling, and SOC platform integrations while guiding teams through MLSecOps, secure\-AI engineering, and responsible AI practices.
Perform code and architecture reviews, provide technical direction for complex ML systems initiatives, including SIEM, SOAR, and EDR ML integrations, cloud\-native ML platforms for security, and GenAI services for analysts, and translate requirements into actionable, measurable implementation plans. Leverage strong software engineering, systems, and communication skills to assess complex security and platform problems, align technical and non\-technical stakeholders, and drive decisions to closure in support of Booz Allen Hamilton's critical enterprise infrastructure, go\-to\-market platforms, and mission operations.
The ideal candidate for our Enterprise Cybersecurity team is technically inclined, intellectually curious, and adaptable, with a strong cyber\-defense mindset. They thrive in a fast\-paced, dynamic environment and are continuous learners who actively seek to understand complex challenges, ask thoughtful questions, and look beyond the obvious to identify innovative and effective ways of working. They bring a security\-first perspective, analytical problem\-solving skills, and the curiosity and aptitude to continuously evolve as threats, technologies, and mission needs change. This position is located in McLean, VA.
What You’ll Work On:
- Design, build, and deploy production AI/ML services for cybersecurity, including supervised and unsupervised detection models, anomaly and behavioral analytics, NLP on security text, retrieval\-augmented generation (RAG) pipelines, agentic workflows, and LLM\-assisted analyst tooling and own them end\-to\-end, data ingest feature pipelines training and tuning packaging deployment serving monitoring retraining.
- Engineer scalable batch and streaming data and feature pipelines over security telemetry including logs, EDR, network, identity, cloud, and threat intel with online and offline parity, feature stores, schema and contract management, and reproducible datasets that power detection, triage, and hunting use cases.
- Build, harden, and operate ML platforms and inference services, including low\-latency real\-time scoring, batch inference, model packaging and containerization, autoscaling, canary and shadow deployments, observability, and rollback, to meet SOC throughput, latency, and reliability SLOs.
- Apply secure\-AI and MLSecOps engineering practices throughout the AI/ML lifecycle, including model and data protection, prompt and inference risk mitigation, evaluation against adversarial inputs such as evasion, poisoning, and prompt injection, model and dataset supply chain security, and responsible AI controls.
- Integrate ML services and analytics into security tools and workflows such as SIEM, SOAR, EDR, IAM, or CSPM via APIs and event\-driven architectures extending detection logic, enrichment, and response playbooks with custom ML/LLM capabilities where commercial tooling falls short.
- Develop automation, scripting, and infrastructure\-as\-code (IaC) to enable repeatable, testable, and version\-controlled ML pipelines, model deployments, and security data integrations across cloud and on\-prem environments.
- Collaborate across data science, platform, data, threat intelligence, and SOC operations teams to deliver end\-to\-end solutions, embed ML practices into DevSecOps and MLSecOps pipelines, and drive implementation through measurable operational outcomes.
Join us. The world can’t wait.
You Have:
- 5\+ years of experience in machine learning engineering, software engineering for ML, or applied AI platform development
- 3\+ years of experience building and operating production ML systems including cybersecurity or security operations
- Experience developing, testing, and integrating ML services across security tools and platforms using APIs, automation, and workflow orchestration and applying AI and machine learning to cybersecurity use cases such as threat and anomaly detection, behavioral analytics, alert triage and prioritization, threat hunting support, analyst copilots, and response automation with measurable impact on SOC outcomes
- Experience software engineering in Python for ML and security use cases, including production\-quality code, design patterns, unit and integration testing, packaging, version control, CI/CD, Docker containerization, and container orchestration including Kubernetes
- Experience working with the modern AI/ML stack, including PyTorch or TensorFlow, scikit\-learn, Hugging Face, LangChain/LlamaIndex, agent frameworks, model serving frameworks, KServe, BentoML, Triton, Ray Serve, embedding\-based retrieval, and vector databases such as pgvector, OpenSearch, Pinecone, Milvus
- Experience operationalizing AI/ML systems (MLOps), model versioning, experiment tracking, feature stores, evaluation harnesses, drift and quality monitoring, and CI/CD for models such as MLflow, Weights \& Biases, SageMaker, Vertex AI, Azure ML, and Kubeflow
- Knowledge of secure AI implementation practices and frameworks including model and data protection, prompt and inference risk, agent guardrails, evaluation against adversarial inputs, ML supply chain security, and governance controls aligned to NIST AI RMF, OWASP LLM Top 10, and MITRE ATLAS
- Knowledge of modern cybersecurity threats and attack patterns, including ransomware, insider threats, credential abuse, data exfiltration, and AI\-enabled attack techniques such as prompt injection, model evasion, data poisoning, and model theft
- Ability to obtain a Secret clearance
- Bachelor's degree
Nice If You Have:
- Experience with programming or scripting languages used in ML, security, and automation environments such as Python, Go, Rust, SQL, PowerShell, and Bash
- Experience designing, deploying, and maintaining enterprise\-scale ML and security systems for sensitive or regulated environments including FedRAMP, IL4, IL5, HIPAA, and PCI
- Experience designing and building agentic AI systems for security operations, multi\-step reasoning, tool and function calling, retrieval pipelines, and human\-in\-the\-loop workflows
- Experience fine\-tuning, distilling, quantizing, or serving LLMs and other models for domain\-specific security tasks, including automated eval harnesses and red\-teaming AI systems
- Experience evaluating and integrating AI\-enabled cybersecurity tooling such as AI\-assisted SIEM, SOAR, UEBA, behavioral analytics, model\-driven detection workflows into enterprise security operations via APIs and event\-driven architectures
- Experience designing and implementing AI/ML services and pipelines over enterprise security telemetry spanning network, endpoint, application, identity, and cloud environments
- Knowledge of AI governance, model risk management, and policy controls aligned to enterprise and regulatory expectations for responsible AI use
- Knowledge of data governance frameworks, data classification standards, and privacy regulations such as GDPR and CCPA
- Knowledge of distributed data and streaming platforms, including Kafka, Kinesis, Spark, and Flink, database structures, data modeling fundamentals, and query optimization, including SQL and NoSQL
- IT Engineering, ML, or Security Certifications such as AWS, GCP, Azure ML Engineer, CKAD, CKA, CISSP, CCSP, CDPSE, cloud security Certifications, or AI security certifications such as ISC2 CAISS or IAPP AIGP Certification
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 $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 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, whether it occurs in person or virtually. 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 facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.
- Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or 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 $77K-$176K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($126K) sits 31% below the category median. Disclosed range: $77K to $176K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Booz Allen Hamilton AI Hiring
Booz Allen Hamilton has 40 open AI roles right now. They're hiring across Data Scientist, Data Engineer, AI Software Engineer, AI/ML Engineer. Positions span McLean, VA, US, Honolulu, HI, US, El Segundo, CA, US. Compensation range: $126K - $303K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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
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