AI Platform Engineer II

Job summary

Supports reliable operation and maintenance of the university’s AI platform, including AI Gateway, API Management, model-serving environments, and local GPU compute for hosting, fine-tuning, training, and inference. The role monitors service health and capacity, supports model lifecycle management, deployments and CI/CD, applies governance, privacy, and security controls including Entra ID and OAuth/OIDC, and maintains operational documentation, dashboards, and reports.

Benefits

Qualifications

• Bachelor’s degree and 2 years of relevant experience
• High School Diploma (or equivalent) and 6 years of relevant work experience in lieu of degree
• Experience supporting enterprise AI, machine learning, cloud, or platform services, including API management, model-serving environments, and application integrations.
• Knowledge of containerization and modern deployment technologies such as Docker, Kubernetes, infrastructure-as-code, CI/CD pipelines, and automated deployment practices.
• Experience administering or supporting Linux-based server environments, GPU-enabled compute infrastructure, high-performance computing resources, or cloud-hosted AI workloads.
• Familiarity with identity and access management technologies, including Microsoft Entra ID, OAuth 2.0, OpenID Connect (OIDC), role-based access controls, and enterprise authentication frameworks.
• Experience utilizing monitoring, observability, and reporting tools to track service health, system performance, capacity utilization, availability, and operational metrics.
• Knowledge of AI governance, data privacy, cybersecurity, and responsible AI principles, including model lifecycle management, auditability, documentation, and compliance with organizational standards.
• Position requires a valid Class E driver’s license.

Responsibilities

• Operate, monitor, and support AI platform services including the AI Gateway, API Management, model-serving endpoints, and related back-end services.
• Assist with operating local GPU nodes and compute environments used for model hosting, fine-tuning, training, and inference workloads.
• Maintain the life cycle of approved local models, including version tracking, deployment support, retirement planning, documentation, and alignment with governance, security, and operational standards.
• Support CI/CD pipelines, infrastructure-as-code configurations, container deployments, observability tooling, and repeatable deployment patterns for AI services and integrations.
• Apply established governance, privacy, and security standards to AI deployments, including authentication, authorization, logging, data handling, and responsible AI controls.
• Maintain architecture diagrams, standard operating procedures, runbooks, model inventories, service dashboards, usage reports, cost reporting, and capacity metrics for AI platform operations.

Skills

Degrees

Work schedule

Travel

Industry

Relocation