AI DevOps Engineer

University of Washington•Seattle, WA

1d•Onsite

About The Position

The University of Washington (UW) is seeking an innovative and experienced AI DevOps Engineer to support its artificial intelligence (AI) initiatives across its three campuses. This pivotal role will shape and implement the university's AI strategy, transforming UW into an AI-powered institution. As a core technical member of the AI Platforms team within UW Information Technology (UW-IT), the engineer will drive the engineering, deployment, administration, and quality assurance of AI-powered applications and services. This role operates within UW-IT's infrastructure umbrella, which provides critical technology support to all three campuses, UW Medicine, and global research operations. The position requires a strong technical foundation in cloud engineering, infrastructure-as-code, CI/CD pipeline development, application administration, and QA/release management, primarily within a Microsoft Azure-centric environment. Deep expertise in Azure architecture, identity and access management, networking, container orchestration, and platform-native services is essential for secure, reliable, and scalable AI application delivery. The role balances engineering velocity with operational stability, security, and cost optimization, emphasizing automation, monitoring, and platform governance. Success also depends on the ability to navigate a decentralized and complex institutional environment, collaborating with 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 for maintaining resilient, compliant, and high-performing AI platform services.

Requirements

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 problem-solving skills with the ability to work effectively under pressure.

Nice To Haves

Microsoft Azure technical certifications (e.g., AZ-400: DevOps Engineer Expert, AZ-305: Azure Solutions Architect Expert, AI-102: Azure AI Engineer Associate).
Equivalent certifications from AWS, GCP, or other cloud providers (demonstrated Azure-specific expertise is strongly preferred).
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.

Responsibilities

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.
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.
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.
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.
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.