AI DevOps Engineer

AI DevOps EngineerAs 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 Washington's 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 UW's IT infrastructure Umbrella that provides critical technology support to all three campuses, UW Medicine, and research operations around the world.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 Azure–centric 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.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-Bachelor's 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