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Booz Allen

Builds AI and cyber systems for the US government

Enterprise Cybersecurity AI Infrastructure Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeMcLean, VA, USA$77.6k - $176k /yrIn-person

About the Job

Requirements

  • At least 5 years of software engineering experience, including building and operating production-scale machine learning infrastructure, data platforms, or distributed systems.
  • At least 2 years of experience creating and training agents and models for agentic AI, including tool calling, evaluation, and deployment into production workflows.
  • Experience with cloud platforms such as AWS and Databricks, and building CI/CD in GitHub Actions with AWS integration, including IAM, OIDC federation, and Secrets Manager.
  • Experience with Python and SQL, infrastructure as code such as Terraform or Terragrunt, and coding agents.
  • Experience building and maintaining data pipelines over large or sensitive datasets, including schema management and reproducible deployments.
  • Experience owning platform work end to end, from technical design through production deployment and operational monitoring.
  • Experience applying DevSecOps practices to delivery pipelines, including least-privilege access, secret handling, and automated security checks.
  • Knowledge of cybersecurity operations or security telemetry.
  • Ability to obtain a Secret clearance.
  • High school diploma or GED.

Responsibilities

  • Partner with AI and ML engineers, data scientists, and SOC operators to translate modeling and analyst requirements into shared platform capabilities, including retrieval, scoring, and agent workflow design.
  • Build, operate, and optimize the platform behind internal AI products, including platforms based on Databricks and AWS.
  • Implement and scale data pipelines to ingest security telemetry and threat intelligence, including logs, endpoint, identity, cloud, and threat-intelligence sources, into governed schemas for analysts and models.
  • Build model evaluation, validation, and serving paths, including experiment tracking, regression checks, and safe rollout and rollback.
  • Build and maintain GitHub Actions CI/CD for AI products, including self-hosted runners, AWS OIDC into IAM, Secrets Manager, pull-request environments, and production promotion.
  • Implement DevSecOps controls, including dependency and secret scanning, infrastructure policy, service-principal isolation, and Unity Catalog grants to prevent non-production identities from accessing production data.
  • Own infrastructure as code for catalogs, permissions, and deployment, such as Terraform or Terragrunt and Databricks Asset Bundles, to make every environment repeatable.
  • Use coding assistants to accelerate delivery and review generated infrastructure, pipeline, and application changes before they merge.
  • Perform design and code reviews, set technical direction for platform work, translate researcher and analyst needs into reusable infrastructure, assess platform problems, align technical and non-technical stakeholders, and drive decisions to closure.

Desired Qualifications

  • Experience with containerization such as Docker and container orchestration such as Kubernetes.
  • Experience with Spark, distributed training, or orchestration frameworks including Ray or PyTorch Distributed.
  • Experience with MLflow or an equivalent stack for experiment tracking, evaluation harnesses, and a model registry on Databricks.
  • Experience implementing retrieval and agent infrastructure, including vector indexes, model endpoints, tool gateways, and evaluation loops.
  • Experience with AWS beyond identity and secrets, across storage, networking, or private connectivity into Databricks.
  • Experience operating platforms in sensitive or regulated environments, including FedRAMP, IL4, or IL5.
  • Knowledge of secure-AI practices aligned to NIST AI RMF, OWASP LLM Top 10, and MITRE ATLAS.
  • Knowledge of SIEM, SOAR, or threat-intelligence platforms and how models are incorporated into analyst workflows.
  • IT, cloud, or security certification such as AWS, Databricks, CKA, CKAD, CISSP, or an AI security certification.

About the Company

Booz Allen

Builds AI and cyber systems for the US government

Booz Allen builds and runs technology for the U.S. government, mostly for the military and intelligence agencies, and its engineers work alongside consultants who know each agency's mission. In 1958, it helped the U.S. Navy create PERT, a project-scheduling method first used on the Polaris submarine missile program and later adopted across government and industry. Its teams design cyber defenses, AI and data tools, and battlefield communications, and it sells its own products, such as Vellox, a suite of AI tools that spot and fight off hackers. Agencies pay mainly through multi-year contracts that reimburse costs, bill hours, or set a fixed price. Booz Allen was founded in 1914 and is based in McLean, Virginia.

Company size
10,001+
Company stage
IPO
Headquarters
McLean, VA
Founded
1914

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