DevOps Engineer

University of Maryland•College Park, MD

1d•$140,000 - $177,966

About The Position

The Applied Research Laboratory for Intelligence & Security (ARLIS) at the University of Maryland is a University-Affiliated Research Center dedicated to advancing research, innovation, and technology transition to improve decision making for U.S. national security. ARLIS brings together scientific expertise and operational insight to address challenges in intelligence analysis, cybersecurity, artificial intelligence and machine learning, quantum science, and human-machine teaming. The DevOps Engineer serves as a technical resource for ARLIS, designing, automating, and maintaining secure, reliable cloud and on-premises computing environments that support research and software development, including machine learning and artificial intelligence workloads. The role develops infrastructure as code, automated build and deployment pipelines, and self-service workflows so researchers can obtain and use computing resources efficiently. In collaboration with researchers, software developers, IT staff, and security personnel, the DevOps Engineer maintains development platforms, monitors system performance, resolves operational issues, and implements improvements that support ARLIS research objectives and applicable University and sponsor requirements. The selected candidate must be able to obtain a U.S. security clearance and meet requirements for access to classified information. The government security clearance investigation includes criminal and credit history checks, as well as verification of U.S. citizenship, birth, education, employment, and military history.

Requirements

Master’s degree from an accredited college or university. Additional qualifying work experience may substitute year for year for up to four years of the required education.
Three years of professional experience in systems engineering, cloud infrastructure, or DevOps engineering.
Knowledge of Azure cloud services and cloud resource lifecycle management, including provisioning, configuration, monitoring, updates, and decommissioning.
Knowledge of Terraform and Infrastructure as Code principles for repeatable, version-controlled infrastructure deployments.
Knowledge of Azure DevOps Pipelines and GitHub Enterprise for source control, continuous integration, continuous delivery, and automated testing.
Knowledge of Sonatype Lifecycle/IQ and Nexus Repository for software composition analysis, policy enforcement, and artifact management.
Knowledge of ServiceNow and integration of requests, approvals, and fulfillment activities into automated or self-service workflows.
Knowledge of Rancher, Docker, Kubernetes, and on-premises GPU infrastructure, including NVIDIA drivers and containerized GPU workloads.
Knowledge of Datadog or comparable observability platforms for metrics, logs, dashboards, monitoring, and alerting.
Knowledge of networking fundamentals and application of cloud security best practices.
Skill integrating enterprise platforms through REST and GraphQL APIs, webhooks, vendor SDKs, and command-line interfaces.
Skill configuring, troubleshooting, and optimizing Linux and Windows Server systems.
Skill applying software development practices, including version control, testing, documentation, and maintainable automation, with an operational mindset.
Ability to design, build, and maintain CI/CD pipelines, process automation, and self-service infrastructure workflows.
Ability to communicate technical information effectively in oral and written form and provide customer-focused service.
Ability to interpret and apply policies, procedures, regulations, and laws.
Ability to manage multiple priorities while supporting production and development services.
Must be able to obtain a U.S. security clearance and meet access requirements for classified information.

Nice To Haves

Degree in Computer Science, Software Engineering, or a related field.
Experience providing technical guidance or mentoring colleagues in DevOps tools, automation, or software delivery practices.
Experience supporting research computing, ML/AI workloads, or shared GPU infrastructure.
Experience delivering technical projects independently and collaboratively while managing competing priorities.

Responsibilities

Design, automate, and maintain secure, reliable cloud and on-premises computing environments supporting research and software development, including machine learning and artificial intelligence workloads.
Develop infrastructure as code, automated build and deployment pipelines, and self-service workflows for researchers.
Maintain development platforms and monitor system performance.
Resolve operational issues and implement improvements supporting ARLIS research objectives and applicable University and sponsor requirements.
Integrate enterprise platforms through REST and GraphQL APIs, webhooks, vendor SDKs, and command-line interfaces.
Support production and development services while managing multiple priorities.