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.