Job Description

Responsibilities*
Prototype and test AI-enabled workflows for CAI projects selected through intake, building lightweight applications, connectors, retrieval pipelines, and automations that integrate with existing CAI systems
Support pilots with domain experts, collect user feedback, diagnose failures, and iterate
Build evaluation datasets, test cases, and repeatable harnesses for quality, reliability, safety, and cost
Implement logging, access controls, guardrails, and operational telemetry in partnership with TIS staff
Establish defined operating work for CAI's local AI server, including model deployment, updates, monitoring, and runbook upkeep
Share practical guidance through demonstrations, office hours, and workshops for staff and student fellows
What You'll Do
The Center for Academic Innovation at the University of Michigan is seeking an Applied AI Engineer for a one-year post-graduate fellowship focused on practical AI prototyping, evaluation, platform operations, and knowledge transfer. Reporting to the Director of Technology Infrastructure and Support, you will work closely with the AI Enablement Lead and TIS colleagues to build and test AI-enabled workflows, integrations, evaluation tools, and technical documentation. The successful candidate is a careful builder who can learn quickly, explain tradeoffs clearly, and turn experiments into maintainable, well-documented assets. You will report to the Director of Technology, Infrastructure and Support.

What makes this fellowship distinctive

You will help operate CAI's dedicated multi-GPU local AI server and the open-source software stack that supports it, gaining experience deploying, evaluating, and monitoring models. Projects across the Center will give you a varied portfolio of applications and reusable tools. You will also share your work through demonstrations, office hours, and workshops. The fellowship combines hands-on delivery, platform operations, and knowledge transfer, with documented project outcomes and a handoff to the next cohort to help build a fellowship alumni community over time.

Required Qualifications*
A bachelor's degree in computer science, information, human-computer interaction, data science, engineering, learning sciences, or a related field, or an equivalent combination of education and applied experience, completed by the start date
Hands-on programming experience in Python, JavaScript, or a comparable language, including experience working with APIs
Experience building applications with large language models, retrieval-augmented generation, structured outputs, agents, or related AI techniques
Experience using Git and working with cloud, container, or modern application-development environments
Experience designing tests or evaluations and interpreting both quantitative results and user feedback
Working knowledge of software security, privacy, access control, and responsible AI practices
Experience with retrieval-augmented generation and agent frameworks, experience with evaluation or observability tooling
Ability to learn and appropriately use new technologies to improve processes, communication, and outcomes, including AI-enabled tools where relevant to the role, consistent with university policy and job responsibilities