AI Agent & Data Quality Intern - Part Time - Spring Semester 2027

Welcome to ATB Technologies, where innovation meets exceptional customer service! We are an award-winning Managed Service Provider (MSP) in St. Louis, fueled by the passion of our team and client referrals. Crafting personalized IT solutions is our forte, and we're on the lookout for enthusiastic individuals to join our crew. At ATB Technologies, it's not just about upgrading businesses with better IT and providing top-tier customer service, it's about creating a workplace for your success.

This part-time internship is ideal for students or recent graduates who are eager to gain hands-on experience working with applied AI in a real-world business environment. The role focuses heavily on data quality, AI-driven documentation improvement, and AI agent testing and QA, with opportunities to experiment, analyze outcomes, and contribute directly to ATB's internal systems and initiatives.

Interns will primarily work in Copilot Studio, supporting efforts to stress test AI agents, improve internal data accuracy, and enhance documentation across core platforms. This internship is best suited for someone who is curious, self-motivated, and interested in understanding how AI tools behave behind the scenes—and how they can be improved through thoughtful testing and iteration.

This internship opportunity will be offered during the Spring Semester of 2027 and is open to eligible college students who meet the program requirements. Interns are expected to work at least 20 hours per week on-site at our Chesterfield office, with flexible scheduling to accommodate class commitments. This is a 12-week internship program with an anticipated start date of 1/18/2027 and end date of 4/9/2027.

Key Responsibilities

• Stress test and experiment with AI agents in real-world scenarios to uncover limitations, edge cases, and unexpected behaviors.

• Investigate how agents reason, make decisions, and use tools in order to identify performance bottlenecks and improvement opportunities.

• Document findings from testing, including failure patterns, successful prompt strategies, and recommendations that influence future agent development.