Platform DevOps Engineer

Gormat•Annapolis Junction, MD

About The Position

The Platform DevOps Engineer will help build and operate USCYBERCOM's AI Enterprise capability, a central hub for moving AI solutions from prototype to secure, governed, enterprise-scale services. The role builds and runs infrastructure, pipelines, and control-plane guardrails to support safe, large-scale AI adoption across the mission. Responsibilities include providing embedded operations support, capability integration, and rapid prototyping for AI-enabled tools; designing, implementing, and maintaining infrastructure for enterprise AI applications in cloud and on-premises, air-gapped, and classified environments; and developing engineering workflows for AI model and agent development, deployment, and maintenance. The engineer will manage CI/CD pipelines, containerization and orchestration, identity and policy controls, approvals, audit and logging, and observability. The role also covers AI model and agent lifecycle management, collaboration with AI/ML engineers and data scientists, infrastructure performance and security, and support for RMF/ATO accreditation of AI systems.

Requirements

Bachelor's degree in a relevant technical field and 10+ years of experience, or a master's degree in a relevant technical field and 8+ 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 ELK stack.
Excellent communication and interpersonal skills, with the ability to collaborate effectively 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 engineering 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, including identity, policy enforcement, approvals, audit/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 performance, security, and scalability of AI infrastructure and support RMF/ATO security accreditation of AI systems.
Research and implement DevOps tools and practices to enhance efficiency.
Research, design, develop, and test operating-systems-level software, compilers, and network distribution software; establish operational specifications and analyze software requirements.