Cybersecurity AI​/ML Engineer

Overview

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. The 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.

• 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 Dev Sec Ops  and MLSecOps pipelines, and drive implementation through measurable operational outcomes.

• 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 Tensor Flow, scikit‑learn, Hugging Face, Lang Chain/Llama Index, agent frameworks, model serving frameworks, KServe, BentoML, Triton, Ray Serve, embedding‑based retrieval, and vector databases such as pgvector, Open Search, 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, Sage Maker, Vertex AI, Azure ML, and Kubeflow.
• Knowledge of secure AI implementation practices and frameworks…