Platform DevOps Engineer

Gormat•Annapolis Junction, MD

About The Position

The Senior Platform / DevOps Engineer will build and operate infrastructure, pipelines, and control-plane guardrails for USCYBERCOM’s AI Enterprise capability, enabling mission teams to move AI solutions from prototype to secure, governed, enterprise-scale services. The role supports AI-enabled tools through embedded operations support, capability integration, and rapid prototyping. The engineer designs and maintains infrastructure for enterprise AI applications across cloud environments, including AWS and Microsoft Azure, as well as on-premises, air-gapped, and classified environments. They develop workflows for AI model and agent development, deployment, and maintenance; manage CI/CD pipelines; implement containerization and orchestration; and build control-plane protections for identity, policy enforcement, approvals, auditing, logging, and observability. The role also covers AI lifecycle management, infrastructure performance and security, support for RMF/ATO accreditation, and ongoing research into DevOps tools and practices. The position additionally includes researching, designing, developing, and testing operating-systems-level software, compilers, and network distribution software; defining operational specifications; analyzing software requirements; and applying computer science, engineering, and mathematical analysis principles. Embedded systems software design may also be involved.

Requirements

Bachelor's degree in a relevant technical field and 10 or more years of experience, or a master's degree in a relevant technical field and 8 or more years of experience.
Active TS/SCI clearance with polygraph.
Advanced proficiency in DevOps principles and practices.
Demonstrated expertise in containerization using Docker and Kubernetes.
Proven experience architecting and managing CI/CD pipelines.
Extensive experience with AI model lifecycle management and maintenance.

Nice To Haves

Familiarity with AWS and Microsoft Azure for infrastructure deployment and management.
Familiarity with monitoring and logging tools such as Prometheus, Grafana, and the ELK stack.
Excellent communication and interpersonal skills and ability to collaborate with cross-functional teams.
Experience with infrastructure-as-code tools such as Terraform and Ansible.
Understanding of machine-learning concepts and their infrastructure implications.
A continuous-learning mindset to stay current with DevOps and AI advancements.

Responsibilities

Provide embedded operations support, capability integration, and rapid prototyping for AI-enabled tools.
Design, implement, and maintain infrastructure for enterprise AI applications in cloud, on-premises, air-gapped, and classified environments.
Develop and optimize workflows and processes for AI model and agent development, deployment, and maintenance.
Architect and manage CI/CD pipelines for AI models, agents, and applications.
Implement and manage containerization and orchestration solutions using Docker and Kubernetes.
Build and maintain control-plane guardrails for identity, policy enforcement, approvals, audit and logging, and observability.
Manage AI model and agent lifecycle activities, including versioning, monitoring, and scaling.
Collaborate with AI/ML engineers and data scientists to streamline deployments and optimize resource utilization.
Oversee the performance, security, and scalability of AI infrastructure.
Support security accreditation of AI systems, including RMF/ATO.
Research and implement DevOps tools and practices to improve efficiency.
Research, design, develop, and test operating-systems-level software, compilers, and network distribution software.
Set operational specifications and formulate and analyze software requirements.
Apply computer science, engineering, and mathematical analysis principles; embedded systems software design may be required.