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# DevSecOps Engineer (AI/ML)

- **Company:** CACI International Inc.
- **Location:** Denver, CO, United States
- **Experience:** Expert
- **Salary:** $90,300.0 - $189,600.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Bash Shell, Continuous Integration, Identity and Access Management, Python (Programming Language), Key Management, Machine Learning, Open Source Technology, Software Tools, Software Deployment, Tripwire, Software Vulnerability Management, Data Logging, Scripting, Retrieval-Augmented Generation, Software Security, Deep Learning, Software Troubleshooting, Generative AI, Agentic-AI, Containerization, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Amazon Bedrock, Deployment Automation, Machine Learning Operations, Terraform, Devsecops, Docker, Vulnerability Analysis, Artifactory, Programming Languages
- **Published:** November 6, 2026
- **Apply:** https://www.careerbuilder.com/job-details/devsecops-engineer-ai-ml-denver-co--e9c1dfc5-7ef2-4e6b-b650-34186307aca7

## About the Role

Artificial Intelligence (AI), Willing to Travel, Business Operations, Artificial Intelligence (AI), Production Systems, Systems Maintenance, Artificial Intelligence (AI) Programming Languages, Emerging Technology, Alliance/Partner Management, Technical Strategy, Required:

 * Bachelor''s degree in a related technical discipline or equivalent experience, with 5+ years of relevant engineering experience.
 * Strong DevSecOps experience, including CI/CD, Infrastructure as Code, automation, and secure software delivery.
 * Experience with Kubernetes, Docker, and production containerized workloads.
 * Experience deploying and supporting applications in AWS, Azure, hybrid, or on-premises environments.
 * Experience with Terraform and scripting languages such as Python or Bash.
 * Experience with identity, secrets management, security scanning, and production observability.
 * Strong troubleshooting, communication, collaboration, and technical documentation skills.
 * Demonstrated passion for AI and willingness to continually learn emerging technologies, including generative AI and agentic AI.
 * Ability to obtain a U.S. Government Top Secret security clearance.

Desired:

 * Candidates with advanced DevSecOps or production AI expertise may be considered for a more senior role based on experience.
 * Experience designing, deploying, and operating production Kubernetes environments.
 * Experience designing reusable Infrastructure as Code, CI/CD frameworks, or cloud deployment architectures.
 * Experience hardening and packaging engineering tools or open-source technologies for production deployment and reuse.
 * Production experience with AI, machine learning, deep learning, computer vision, or generative AI systems.
 * Experience with AI services and engineering technologies such as Amazon Bedrock, SageMaker, Azure AI Foundry, MLOps tooling, model serving, RAG, or agentic AI.
 * Experience with software supply chain security technologies such as Harbor, Anchore, Black Duck, Trivy, or Artifactory.
 * Experience supporting DoD or Federal environments, including RMF and ATO processes.
 * Active TS/SCI clearance.
 * What You Can Expect:

A culture of integrity.

## Description

We''re looking for an engineer who is passionate about AI and enjoys applying modern DevSecOps practices to build, deploy, secure, and sustain production AI systems. You''ll help operate and improve the Center''s internal tools and infrastructure, develop reusable engineering accelerators, and partner with programs to integrate AI into mission environments.

You''ll also have the opportunity to help shape CACI''s broader AI technology direction by contributing to the evaluation and selection of emerging technologies, informing strategic technology partnerships, and influencing the enterprise capabilities we develop to accelerate AI adoption across CACI.

Responsibilities:

 * Operate, secure, and improve the AI Center of Excellence''s internal development infrastructure and engineering tools.
 * Build and maintain secure CI/CD pipelines, Infrastructure as Code, deployment automation, and reusable infrastructure modules.
 * Deploy, operate, and troubleshoot containerized applications using Kubernetes and Docker across cloud, on-premises, and classified environments.
 * Integrate security, identity and access management, vulnerability management, and software supply chain controls throughout the delivery lifecycle.
 * Implement monitoring, logging, tracing, and other observability capabilities for production systems.
 * Partner with program engineering teams to integrate and operationalize AI capabilities in customer environments.
 * Develop reusable AI deployment accelerators, reference implementations, engineering patterns, and technical guidance based on lessons learned across programs.