My initial motivation in joining the AI Innovation Lab as a tech lead was straightforward: to ensure the AI work I was doing at SSIT aligned with broader Cornell efforts. What started as a coordination exercise became something more valuable: exposure to problems and tools I wouldn’t encounter in my regular work building websites.

As a tech lead, I’ve watched the AI toolset evolve from Streamlit apps to the current platform, N8N. I’ve worked on several experiments, including the Payflow project, and have onboarded multiple SCL (Student & Campus Life) AI experiments. Along the way, I’ve built my first RAG system, implemented AI enterprise web search, and developed advanced AI OCR PDF processing—all while getting early access to the Innovation Hub’s latest tools and offerings.

What does a tech lead actually do?

Tech leads are the bridge between academic projects and production-ready systems. We’re professionals from across Cornell IT who volunteer our time to guide student teams through the process of building real AI solutions.

The work varies. Sometimes it’s technical—defining specifications, integrating with Cornell’s AI infrastructure (N8N, LiteLLM, AI Gateway), building systems with proper error handling. Sometimes it’s more about facilitation—guiding teams through weekly sprints with stakeholders, helping students think through architectural decisions. And sometimes it’s about seeing immediate impact. In the Payflow experiment I worked on, I could see how we were improving process standardization in real time.

I’ve also been able to onboard several SCL (Student & Campus Life) AI experiments and serve as their tech lead, which has given me exposure to different problem domains and approaches.

Why volunteer as a tech lead?

As a tech lead, I’ve had early access to all the latest tools and offerings at Cornell. That means hands-on experience with the AI Gateway, Agent Studio (N8N), and LiteLLM before they’re widely available.

But the bigger value has been exposure to problems I wouldn’t see otherwise. In my regular job, I’m building websites. In the Hub, I hear about tedious work that gets in the way of higher-level tasks—the kind of user stories that help you understand what actually needs solving. That perspective has been useful.

Beyond personal learning, tech leads help shape how AI gets deployed across Cornell. The experiments we guide often become production systems that reduce manual work, improve service delivery, and set standards for responsible AI use. When a tech lead helps students build a working solution, we’re not just mentoring—we’re directly contributing to Cornell’s AI capabilities and helping the university move faster on emerging technology.

Every other Thursday, the Hub hosts the AI Exploration Series, drop-in virtual sessions where tech leads, students, and AI engineers discuss everything from basic prompting to advanced agentic workflows. The conversations are relaxed and usually uncover something unexpected.

One Cornell, many builders

The AI Innovation Hub embodies the “One Cornell” vision. People from across the university come together to solve problems that matter. Tech leads represent departments and units throughout Cornell, bringing diverse perspectives and expertise to each project.

The students earn independent study credit to work alongside tech leads who build and deploy enterprise-grade solutions in their day jobs. That pairing helps transform academic projects into production systems. For students, the payoff is tangible: a working demo of software currently in use, not just a line on a CV.

Projects are selected based on impact, feasibility, alignment with institutional priorities, and ethical responsibility. Recent examples include:

• VERA (Values Exploration and Reflection Assistant) helping 900 first-year engineering students reflect on their values, logging over 40,000 interactions with zero support requests
• Socratic Chatbot for Classroom Learning helping 300 students in a climate course think critically, now integrated into Canvas LMS
• Campus Groups Audit Automation saving 30+ minutes per submission on 10,000 reimbursement requests per semester

What it takes (and what it doesn’t)

You don’t need to be an AI expert. You need to be curious, willing to learn, and comfortable working with ambiguity. The time commitment is real—it’s work on top of your regular job. But the learning happens naturally when you’re working on real problems with real constraints.

The impact is visible. In the Payflow experiment, I could see immediate results in process standardization. When students present a working system that’s already in production, the extra effort feels worthwhile.

Interested in learning more?

If you’re Cornell faculty or staff and curious about AI, tech lead opportunities open each semester. You’ll get early access to Cornell’s AI Platform and work alongside graduate students on real problems.

Reach out to Ayham Boucher, head of AI Innovation, through the IT Service Desk, or email ailab@cornell.edu to learn more. You can also join the conversation in Microsoft Teams: GenAI at Cornell: General.