Enterprise Cybersecurity AI Infrastructure Engineer

Booz Allen Hamilton is hiring an Enterprise Cybersecurity AI Infrastructure Engineer to build, scale, and operationalize an enterprise Cyber Operations AI infrastructure platform that supports training, evaluation, hosting, and serving of AI capabilities.

Role Responsibilities

• Serve as a hands-on technical contributor for building, scaling, and operationalizing Booz Allen’s Cyber Operations AI infrastructure platform, covering training, evaluation, hosting, and serving of AI capabilities.
• Lead key platform components, including Databricks for data and model delivery and GitHub with AWS to enable the deployment pathway for internal AI products into production.
• Streamline collaboration between development and operations to help systems and workflows move efficiently.
• Partner with AI/ML engineers, data scientists, and security operators to convert security telemetry, threat intelligence, and analyst workflows into dependable, scalable pipelines and services.
• Build and run CI/CD and DevSecOps for these products, including GitHub Actions, infrastructure as code, identity and secrets management, and least-privilege access using Unity Catalog.
• Conduct design and code reviews, provide technical direction for platform work, and turn researcher and analyst needs into reusable infrastructure.
• Assess platform issues, coordinate technical and non-technical stakeholders, and drive decisions to closure to support Booz Allen cyber operations and mission platforms.
• Work with AI/ML engineers, data scientists, and SOC operators to deliver 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 (logs, endpoint, identity, cloud, and threat-intel sources) into governed schemas for analysts and models.
• Create model evaluation, validation, and serving paths, including experiment tracking, regression checks, and safe rollout and rollback to meet quality expectations for operational use.
• 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 deployments using Terraform or Terragrunt and Databricks Asset Bundles to keep environments repeatable.
• Use coding assistants to accelerate delivery and review generated infrastructure, pipeline, and application changes before merging.

Required Qualifications

• 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 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, and working with infrastructure as code including Terraform or Terragrunt while 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 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 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.

Security Clearance

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

AI Usage Policy for Interviews

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 and Location

• Location: McLean, VA (onsite)
• Onsite roles primarily perform full-time work at a customer facility, with collaboration as required by the role.

Identity Verification

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

Compensation and Posting Timeline

• Projected compensation range: USD 62,000 to 141,000 per year (annualized).
• This posting will close within 90 days from the Posting Date.

Non-Discrimination Commitment

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.