Enterprise Cybersecurity AI Infrastructure Engineer

Are you technically inclined, intellectually curious, and adaptable, with a strong cyber-defense mindset? Do you thrive in a fast-paced, dynamic environment, seek to understand complex challenges, ask thoughtful questions, and look beyond the obvious to identify innovative and effective ways of working? Do you bring a security first perspective, analytical problem-solving skills, and curiosity along with aptitude to continuously evolve as threats, technologies, and mission needs change?

As a Cybersecurity AI Infrastructure Engineer, you will operate as a hands-on technical contributor responsible for building, scaling, and operationalizing for Booz Allen's Cyber Operations AI infrastructure platform, supporting training, evaluating, hosting, and serving of AI capabilities. In this role, you will lead key components of the platform, including Databricks for data and model delivery and Git Hub with AWS for the deployment pathway for moving internal AI products into production.

You will streamline collaboration between development and operations, ensuring systems and workflows move efficiently, work with AI/ML engineers, data scientists, and security operators, to transform security telemetry, threat intelligence, and analyst workflows into reliable, scalable pipelines and services. You will also build and operate CI/CD and Dev Sec Ops  for these products, including Git Hub Actions, infrastructure as code, identity, secrets, and least-privilege access in Unity Catalog.

You will perform design and code reviews, set technical direction for platform work, and turn researcher and analyst needs into reusable infrastructure, leverage strong software engineering and communication skills to assess platform problems, align technical and non-technical stakeholders, and drive decisions to closure in support of Booz Allen's cyber operations and mission platforms.

Partner with AI and ML engineers, data scientists, and SOC operators to turn 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 land security telemetry and threat intelligence including logs, endpoint, identity, cloud, and threat-intel sources into governed schemas analysts and models can use.

Build model evaluation, validation, and serving paths, including experiment tracking, regression checks, and safe rollout and rollback, so models reach operations with a measured quality bar. Build and maintain Git Hub Actions CI/CD for AI products including self-hosted runners, AWS OIDC into IAM, Secrets Manager, pull-request environments, and production promotion. Implement Dev Sec Ops  controls, including dependency and secret scanning, infrastructure policy, service-principal isolation, and Unity Catalog grants to keep non-production identities off production data.

Own infrastructure as code for catalogs, permissions, and deployment such as Terraform or Terragrunt, and Databricks Asset Bundles so every environment is repeatable. Use coding assistants to accelerate delivery, and review generated infrastructure, pipeline, and application changes before they merge.

• 5+ years of experience software engineering including building and operating production-scale machine learning infrastructure, data platforms, or distributed systems
• 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 like AWS and Databricks and building CI/CD in Git Hub Actions with AWS integration, including IAM, OIDC federation, and Secrets Manager
• Experience in Python and SQL, and working with infrastructure as code such as Terraform or Terragrunt while also utilizing 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 Dev Sec Ops  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
• HS…