DevOps Engineer

Booz Allen Hamilton•McLean, VA

3d•$77,600 - $176,000

About The Position

Booz Allen is seeking a DevOps Engineer to help build next-generation cybersecurity products, modernize systems with artificial intelligence, and develop new capabilities. The role focuses on deploying and operating secure, reliable, and highly available software in cloud environments, from pilots through critical production systems. The engineer translates business and development team requirements into automated platform solutions and collaborates with DevOps, platform engineering, development, customer infrastructure, and security teams in a fast-paced Agile environment. The position involves cloud-native application and infrastructure operations, CI/CD pipelines, container and Kubernetes environments, DevSecOps controls, and deployment of AI-enabled applications. Work may include supporting enterprise solutions in customer-managed, self-hosted, or on-premises environments and resolving environment-specific deployment issues. A Secret clearance and a bachelor's degree are required.

Requirements

8+ years of experience developing and implementing DevSecOps tools, automation, and processes.
8+ years of experience troubleshooting, triaging, and resolving complex CI/CD pipeline issues.
Experience with Kubernetes, Docker, and CI/CD platforms such as Jenkins, GitHub Actions, or GitLab.
Experience securing containerized workloads, including container image security, vulnerability scanning, secrets management, and runtime security.
Experience deploying and supporting enterprise software in customer-managed, self-hosted, or on-premises environments.
Experience implementing DevSecOps security controls and automated compliance checks, including policy enforcement, vulnerability management, and audit evidence collection.
Experience deploying and operationalizing AI-enabled applications in enterprise environments.
Experience collaborating with customer infrastructure, security, and platform engineering teams.
Secret clearance required.
Bachelor's degree required.

Nice To Haves

Experience with cloud providers such as AWS or Microsoft Azure.
Experience integrating AI-driven capabilities into compliance, governance, risk management, or security workflows, including automated control validation, evidence collection, policy mapping, and compliance reporting.
Experience deploying and maintaining AI-powered solutions in self-hosted, on-premises, or customer-managed environments, including restricted-network or air-gapped environments.
Experience operationalizing AI applications in production, including health monitoring, logging, observability, performance troubleshooting, dependency management, and deployment lifecycle management.
Experience deploying Kubernetes-based applications using Helm or similar tools, including private container registries, persistent storage, network policies, and cluster-level security controls.
Experience working directly with customer security, infrastructure, and platform engineering teams to validate deployment architectures, resolve environment-specific issues, and meet enterprise security and compliance requirements.

Responsibilities

Develop and implement DevSecOps tools, automation, and processes to improve software delivery maturity, security posture, and operational reliability.
Troubleshoot, triage, and resolve CI/CD pipeline issues, including build and deployment failures, performance bottlenecks, and infrastructure issues.
Design, deploy, and maintain cloud-native applications and infrastructure using Kubernetes, Docker, and CI/CD platforms.
Develop, manage, harden, and secure containerized workloads, including image security, vulnerability scanning, secrets management, and runtime security.
Deploy and support enterprise software in customer-managed, self-hosted, or on-premises environments, including infrastructure dependencies and system integrations.
Implement DevSecOps security controls and automated compliance checks throughout the software delivery lifecycle, including policy enforcement, vulnerability management, and audit evidence collection.
Deploy and operationalize AI-enabled applications in enterprise environments, managing dependencies, securing model and data access, and supporting reliable, repeatable deployments.
Collaborate with customer infrastructure, security, and platform engineering teams to resolve deployment issues, meet security requirements, and support production adoption.