AI DevOps Engineer

Job Description

Position Purpose:

As a UW employee you have a unique opportunity to change lives on our campuses in our state and around the world. UW employees offer their boundless energy creative problem-solving skills and dedication to build stronger minds and a healthier world. By being deeply invested in our work showing compassion in our interactions and embodying the spirit of a team player each member contributes to a thriving community. UW is committed to attracting and retaining a diverse staff; your experiences perspectives and unique identities will be honored at the University of Washington. Together our community strives to create and maintain working and learning environments that are inclusive equitable and welcoming.

University of Washington is at the forefront of leveraging cutting-edge technologies to transform education research and healthcare. UW Information Technology (UW-IT) is the central IT organization for the University of Washington collaborating with partners across the University community to advance
teaching learning innovation and discovery. UW-IT delivers critical IT services and support to all three campuses UW medical centers and global research operations. Innovation and discovery are at the heart of what UW-IT does and drive the work in advancing the University of Washingtons role and mission.

We are seeking an innovative and experienced AI DevOps Engineer to support the artificial intelligence (AI) initiatives at the university and its three campuses. This role is a pivotal role in shaping and implementing our AI strategy to transform UW into an AI-powered University. As a core technical member of the AI Platforms team the AI DevOps Engineer drives the engineering deployment administration and quality assurance of AI-powered applications and services across the university. The AI DevOps Engineer role will work within Service Management and AI Platform team under the UWs IT infrastructure Umbrella that provides critical technology support to all three campuses UW Medicine and research operations around the world.

Position Complexities

The AI DevOps Engineer role requires a strong technical foundation spanning cloud engineering infrastructure-as-code CI/CD pipeline development application administration and QA/release management within a Microsoft Azurecentric environment. Deep hands-on expertise in Azure architecture identity and access management networking container orchestration and platform-native services is essential to ensure secure reliable and scalable delivery of AI applications across the university. This role balances engineering velocity with operational stability security and cost optimization while maintaining high standards for automation monitoring and platform governance.

Success in this position also depends on the ability to operate effectively within a decentralized and complex institutional environment. The AI DevOps Engineer must collaborate across AI research development IT operations and information security teams to promote consistent DevOps practices strengthen release discipline and align cloud implementations with institutional strategy. Strong communication proactive risk management and continuous improvement are critical to maintaining resilient compliant and high-performing AI platform services.

Position Dimensions and Impact to the University

The AI DevOps Engineer serves as a key technical contributor on the AI Platforms team responsible for the engineering deployment administration and quality assurance of AI applications and services at the university. This role combines a strong engineering foundation with hands-on application administration deep Microsoft Azure cloud platform expertise and QA/release management practices to ensure reliable secure and scalable delivery of AI solutions. The AI DevOps Engineer works collaboratively with cross-functional teams to build and maintain CI/CD pipelines manage cloud infrastructure administer AI platform applications and drive continuous improvement in development and release processes.

Position Responsibilities

25% Engineering & Development

-Design develop and maintain infrastructure-as-code (IaC) solutions using tools such as Terraform Bicep or ARM templates to provision and manage Azure cloud resources for AI platforms and services.
-Build and maintain CI/CD pipelines (e.g. Azure DevOps GitHub Actions) to automate the build test and deployment of AI applications and microservices.
-Develop scripts automation tools and utilities (e.g. PowerShell Python Bash) to streamline operational tasks monitoring and incident response.
-Collaborate with AI developers and data engineers to containerize applications (Docker Kubernetes/AKS) and optimize deployment architectures for performance and cost efficiency.
-Contribute to the development of APIs integrations and middleware that connect AI services with existing university IT systems and data sources.
-Participate in code reviews pair programming and technical design discussions to maintain high engineering standards across the team.

25% Application Administration & Azure Platform Management

-Administer and maintain AI platform applications including configuration management user access provisioning patching upgrades and performance tuning.
-Manage and monitor Azure cloud environments (e.g. Azure App Services Azure AI Services Azure SQL Azure Storage Azure Virtual Networks) ensuring availability security and compliance with university policies.
-Implement and manage identity and access management (IAM) solutions using Azure Active Directory (Entra ID) role-based access controls and conditional access policies.
-Monitor application and infrastructure health using Azure Monitor Log Analytics Application Insights and other observability tools; triage and resolve incidents promptly.
-Manage Azure resource costs through rightsizing reserved instances and budget alerting; provide regular reporting on cloud spend and optimization opportunities.
-Maintain comprehensive documentation of system architectures configurations runbooks and standard operating procedures.

20% QA & Release Management

-Define and implement QA strategies for AI applications including automated testing frameworks (unit integration regression performance) integrated into CI/CD pipelines.
-Develop and manage release processes schedules and deployment plans to ensure smooth predictable and low-risk releases to production environments.
-Coordinate release activities across development QA and operations teams; serve as the release manager for AI platform deployments.
-Establish and maintain environment management practices across development staging and production environments to ensure consistency and reliability.
-Track and report on quality metrics release cadence deployment success rates and incident trends; drive continuous improvement initiatives based on data.
-Conduct post-release validation smoke testing and rollback procedures as needed to maintain service quality and reliability.
-Provide ongoing testing monitoring observability and post-deployment troubleshooting support to ensure optimal performance and customer satisfaction.

15% Security Compliance & Governance

-Implement security best practices across the DevOps lifecycle including secret management vulnerability scanning container security and network security configurations in Azure.
-Support compliance with university data governance policies FERPA and other regulatory requirements as they pertain to AI applications and cloud infrastructure.
-Collaborate with university information security teams to conduct security assessments address audit findings and remediate vulnerabilities in a timely manner.
-Participate in AI governance activities ensuring that deployed AI solutions adhere to ethical guidelines data privacy regulations and institutional policies.
-Implement and maintain disaster recovery and business continuity plans for AI platform services.

15% Collaboration Documentation & Continuous Improvement

-Collaborate with cross-functional teams including AI researchers data scientists software engineers and IT operations staff to align DevOps practices with team and university goals.
-Provide technical guidance and mentorship to team members on DevOps best practices Azure services and release management methodologies.
-Evaluate emerging DevOps tools cloud services and automation technologies; make recommendations for adoption to improve efficiency and quality.
-Contribute to the development of internal knowledge bases training materials and technical documentation to enhance team capabilities and institutional knowledge.
-Participate in agile ceremonies (sprint planning retrospectives stand-ups) and contribute to process improvement initiatives across the AI Platforms team.

Position Qualifications

- Bachelors degree in Computer Science Information Technology Software Engineering or a related field or equivalent combination of education and experience.

- 3 years of experience in a DevOps Site Reliability Engineering (SRE) or software engineering role with a focus on cloud platforms.
-Demonstrated hands-on experience with Microsoft Azure cloud services (compute networking storage identity and AI/ML services).
-Strong working knowledge of Azure architecture patterns governance models and platform-native services.
-Hands-on experience building and managing CI/CD pipelines using Azure DevOps GitHub Actions or similar tools.
-Experience with infrastructure-as-code tools (Terraform Bicep ARM templates).
-Proficiency in scripting/programming languages such as Python PowerShell or Bash.
-Experience with containerization technologies (Docker Kubernetes/AKS).
-Experience with QA methodologies automated testing frameworks and release management processes.
-Strong troubleshooting and problem-solving skills with the ability to work effectively under pressure.

Desired Experience:

-Strongly preferred: Microsoft Azure technical certifications (e.g. AZ-400: DevOps Engineer Expert AZ-305:Azure Solutions Architect Expert AI-102: Azure AI Engineer Associate.)
-DevOps Engineer Expert AI-102 Azure AI Engineer Associate or comparable role-based Azure certifications).
-Candidates holding equivalent certifications from AWS GCP or other cloud providers are encouraged to apply; however demonstrated Azure-specific expertise is strongly preferred. Azure architecture governance and service design differ significantly from other cloud platforms and direct Azure experience is highly valued.
-Experience in a higher education or public sector IT environment.
-Experience with AI/ML platforms model deployment and MLOps practices.
-Familiarity with monitoring and observability tools (Azure Monitor Grafana Datadog or similar).
-Experience with Agile/Scrum methodologies and project management tools (e.g. Azure Boards Jira).
-Knowledge of data governance FERPA and security compliance frameworks relevant to higher education.

Working Environmental Conditions

Work in an open office environment and contribute to collaborative teamwork focused on problem-solving. Daily interactions with other team members subject matter experts and stakeholders at all levels of the organization. While the general working hours are within Monday through Friday 8 a.m.-5 p.m. the AI DevOps Engineer will on occasion need to adjust hours to accommodate the business needs and deadlines. Attend and occasionally present at conferences.

Other Comments

A satisfactory outcome from a criminal history verification may be required prior to hire.

Compensation Benefits and Position Details

Pay Range Minimum:

Pay Range Maximum:

Other Compensation:

Benefits:

Temporary or Regular

FTE (Full-Time Equivalent):

Union/Bargaining Unit:

About the UW

Working at the University of Washington provides a unique opportunity to change lives on our campuses in our state and around the world.

UW employees bring their boundless energy creative problem-solving skills and dedication to building stronger minds and a healthier return they enjoy outstanding benefits opportunities for professional growth and the chance to work in an environment known for its diversity intellectual excitement artistic pursuits and natural beauty.

Our Commitment

The University of Washington is committed to fostering an inclusive respectful and welcoming community for all. As an equal opportunity employer the University considers applicants for employment without regard to race color creed religion national origin citizenship sex pregnancy age marital status sexual orientation gender identity or expression genetic information disability or veteran status consistent with UW Executive Order No. 81.

To request disability accommodation in the application process contact the Disability Services Office at or .

Applicants considered for this position will be required to disclose if they are the subject of any substantiated findings or current investigations related to sexual misconduct at their current employment and past employment. Disclosure is required under Washington state law.