Enterprise Cybersecurity AI Infrastructure Engineer

Booz Allen Hamilton is seeking an Enterprise Cybersecurity AI Infrastructure Engineer to build, scale, and operationalize an enterprise Cyber Operations AI infrastructure platform. The role focuses on platform components and delivery pathways that support training, evaluation, hosting, and serving of AI capabilities with CI/CD and DevSecOps practices.

Responsibilities

• Serve as a hands-on technical contributor building, scaling, and operationalizing Booz Allen’s Cyber Operations AI infrastructure platform, supporting training, evaluating, hosting, and serving AI capabilities.
• Lead key platform components, including Databricks for data and model delivery and GitHub with AWS to support deployment pathways for moving internal AI products into production.
• Streamline collaboration between development and operations to improve workflow throughput and system movement.
• Collaborate with AI/ML engineers, data scientists, and security operators to convert security telemetry, threat intelligence, and analyst workflows into reliable, scalable pipelines and services.
• Build and operate CI/CD and DevSecOps capabilities for products, including GitHub Actions, infrastructure as code, identity and secrets management, and least-privilege access using Unity Catalog.
• Conduct design and code reviews, set technical direction for platform work, and translate researcher and analyst needs into reusable infrastructure.
• Assess platform issues, align technical and non-technical stakeholders, and drive decisions to closure in support of cyber operations and mission platforms.
• Partner with AI/ML and SOC operators to deliver shared platform capabilities, including retrieval, scoring, and agent workflow design.
• Build, operate, and optimize the infrastructure behind internal AI products, including platforms based on Databricks and AWS.
• Implement and scale data pipelines to ingest security telemetry and threat intelligence from logs, endpoint, identity, cloud, and threat-intel sources into governed schemas usable by analysts and models.
• Create model evaluation, validation, and serving paths, including experiment tracking, regression checks, and safe rollout and rollback to meet defined quality requirements for operations.
• 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 such as 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 repeatability using Terraform or Terragrunt and Databricks Asset Bundles.
• Use coding assistants to accelerate delivery and review generated infrastructure, pipeline, and application changes before merging.

Requirements

• 2+ years of experience with 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 production deployment workflows.
• Experience with 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, and working with infrastructure as code using 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 DevSecOps practices, including least-privilege access, secret handling, and automated security checks.
• Knowledge of cybersecurity operations or security telemetry.
• Ability to obtain a Secret clearance.
• HS diploma or GED.

Technologies

• Databricks
• GitHub
• AWS
• GitHub Actions
• AWS OIDC
• IAM
• Secrets Manager
• Unity Catalog
• CI/CD
• DevSecOps
• Infrastructure as code
• Terraform
• Terragrunt
• Databricks Asset Bundles
• Python
• SQL
• Coding assistants

Benefits

• Health insurance
• Life insurance
• Disability insurance
• Financial benefits
• Retirement benefits
• Paid leave
• Professional development
• Tuition assistance
• Work-life programs
• Dependent care
• Recognition awards program acknowledging employees for exceptional performance and superior demonstration of values

Nice If You Have

• Experience with containerization such as Docker and container orchestration such as Kubernetes.
• Experience with Spark, distributed training, or orchestration frameworks such as 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 incorporate into analyst workflows.
• IT, cloud, or security Certification such as AWS, Databricks, CKA, CKAD, CISSP, or an AI security Certification.

Clearance

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information.

Candidate AI Usage Policy

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

Onsite: work will primarily be performed full-time at a customer facility in McLean, VA, where employees collaborate directly with colleagues and customers as required by the role.

Identity Statement

• During the hiring process, candidates will be asked to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud.
• Candidates are expected to be on camera during interviews and assessments.
• Booz Allen reserves the right to take a picture to verify identity and prevent fraud.

Compensation and Posting Timeline

• Projected compensation range: $62,000.00 to $141,000.00 (annualized USD).
• This posting will close within 90 days from the Posting Date.

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.