Cybersecurity AI/ML Engineer

About the role

The Cybersecurity AI/ML Engineer role involves 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.

Responsibilities

• 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.

Requirements

• 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.
• 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 ATLASKnowledge 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.

Qualifications

• Bachelor's degree.

Skills

• Strong software engineering, systems, and communication skills.
• Technical and non-technical stakeholder alignment skills.
• Continuous learning and adaptability to evolving threats and technologies.
• Security-first mindset and analytical problem-solving skills.

Benefits

At Booz Allen, we offer a comprehensive benefits package designed to support your total well-being. This includes 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.

Pay

The projected compensation range for this position is $99,000.00 to $225,000.00 (annualized USD).

Schedule

This position is located in McLean, VA.