“That project changed how I talk with my engineers about AI work, because now I’ve felt the trade-offs myself.” Luis Toruno had learned to step back from the code. Early in management, he discovered that jumping in to fix things himself could slow his team down. Today, he leads four teams at Wells Fargo, where much of his work centers on architecture and people. As AI changes how his engineers work, he wants practical experience to help him guide them.
That motivation brought the Quantic Executive MBA alumnus back for Quantic’s MS in AI Engineering. Building an HR policy assistant for the AI Engineering Techniques & Architectures project meant taking on the development work himself. His Quantic MSAIE studies were also challenging a familiar instinct: starting with the architecture. A florist case study and a peer’s perspective prompted him to reconsider what needed to happen before he began designing a solution.
Quick Answer: What is Luis bringing from Quantic MSAIE to his engineering teams?
“The biggest change has been in how I scope AI work, more than in any code I’ve written.”
Luis Toruno’s studies in Quantic’s MS in AI Engineering are changing how he responds to AI requests at Wells Fargo. He now pauses before sketching an architecture to establish what the system should help people do. Building his own application has also given him experience to draw on when discussing AI work with his engineers.
- A florist case study and a peer’s questions helped Luis see how his engineering priorities could overlook a business owner’s concerns.
- For a Quantic MSAIE course project, he built and deployed an HR policy assistant for a fictional company, taking responsibility for the technical decisions and working through problems himself.
- He has started using an adapted course requirements template with his technical leads. Their discussions begin with the decision an AI system should improve, the data available, and what a small first version could look like.
What changed when Luis moved from writing code to leading engineers?
“The biggest turning point in my career was the day I stopped being the person who wrote the code.”
At Citrix, Luis progressed from network engineering into software development before moving into management. The adjustment was harder than he expected. “For years I’d been valued for solving hard problems myself. Suddenly my job was to help other people solve them.”
In his first months as a manager, he would still jump into code reviews to fix things. It took time to recognize that stepping in was slowing his team down. He needed a different way to judge whether he was doing his job well.
“What changed was learning to measure my impact by how much my team grew, not by how much I personally shipped.”
He carried that outlook into management roles at LogMeIn and GoTo, and then to Wells Fargo, where he now leads four teams. Helping other engineers do their work had become central to his own.
As AI began changing that work, Luis wanted experience he could draw on when guiding his teams. Returning to hands-on learning would let him work through the challenges himself. That motivation helped bring him back to Quantic.
Why did Luis pursue Quantic’s MS in AI Engineering after his EMBA?
“I wanted to get my hands dirty again so I can guide my teams with real experience, not just opinions.”
Luis completed his Quantic Executive MBA in 2024. When he returned to study AI engineering, he wanted to learn how to design, build, and deploy AI systems himself. Those skills would support another priority: leading AI adoption and implementation within his organization.
A second degree also had to fit into his life. Luis works full-time and has a busy family life at home. Quantic’s format allowed him to make room for study in shorter stretches.
“With Quantic, I can learn in short, focused sessions and then apply something the same week.”
That practical, applied style was a major reason he came back. He also recognized conversations from his working life in the Quantic MSAIE coursework. With stakeholders, he regularly discusses what they are actually building and how to deliver it in stages. His studies gave him opportunities to work through those questions himself, from defining requirements to planning delivery.
“I also like that the coursework doesn’t treat engineering and business as separate worlds.”
What did a florist case study in Quantic MSAIE teach Luis?
“We went through four versions of the requirements doc. Each version had less “AI” in it and more of the actual business problem.”
In the Managing AI Engineering Projects course, Luis worked through an AI initiative for a small florist shop. He approached it with the priorities familiar from platform engineering at a large bank: architecture, scalability, and choosing the right model.
His peer Ben Margolin brought the discussion back to the shop owner. “Ben kept pulling me back to simpler questions. What does the owner actually lose sleep over? What happens to the flowers that don’t sell by Friday?”
Those questions changed the conversation. As they revised the requirements, the owner’s needs became more prominent. Luis was reconsidering where an AI project should begin. The architecture and model choices could follow once the business problem was clear.
He now asks his own teams “what decision does this actually improve?” much earlier in their discussions. It is a question he traces directly to that exchange with Ben.
“I got that habit from a florist case study and from Ben, who wouldn’t let me skip the basics.”
What did Luis learn by building an HR policy assistant in Quantic MSAIE?
“It’s one thing to approve a design doc for an AI system. It’s another to debug why your agent picked the wrong tool at 11 p.m.”
For the AI Engineering Techniques & Architectures project, Luis built an HR policy assistant for a fictional company. The application retrieves information from 17 policy documents to inform its answers, using an approach called retrieval-augmented generation, or RAG.
Getting it to work meant making decisions throughout the application. Luis had to consider how to divide the documents into searchable sections and whether the assistant was finding useful information. When the agent selected the wrong tool, he had to investigate what had happened.
Luis took responsibility for chunking strategy, retrieval quality, testing, and deployment, including working through problems with the free-tier model.
He also tested and deployed the application, using GitHub Actions to automate testing and deployment. The project took him through the practical work behind the kinds of design discussions he has as an engineering leader.
“What stood out was how close it felt to real work.”
Working through those decisions himself gave Luis something he wanted to bring back to his teams: experience of the trade-offs involved.
How is Luis applying his Quantic MSAIE learning at Wells Fargo?
“The Managing AI Engineering Projects course pushed me to slow down.”
At Wells Fargo, AI requests often arrive with a proposed solution already attached. Someone wants a chatbot or suggests using a large language model (LLM): “Can we add a chatbot?” or “Can we use an LLM for this?” Luis’s instinct used to be to start sketching an architecture right away.
Now, he pauses before beginning the design. He wants to establish what the proposed system should help people do and what the team has available to build it. He starts with three questions: “What decision is this supposed to improve? What data do we actually have? And what does a small first version look like?”
He has also brought a resource from his coursework into those discussions. “I’ve started using a lightweight version of the requirements template from the course with my tech leads.” The template gives him a way to put that change into practice when the next AI request arrives.
Bringing firsthand AI experience to his leadership
“The tools are changing how engineers work, and I didn’t want to lead that change from the sidelines.”
When Luis moved into management, he learned to measure his contribution through his team’s growth. Returning to hands-on learning through Quantic’s MS in AI Engineering reflects that same priority. He wants to guide his engineers with experience he has gained himself.
Building the HR policy assistant has already changed the conversations he has with them. He has worked through implementation trade-offs firsthand, while his coursework has prompted him to pause before designing a solution. He brings both experiences to his leadership: understanding what an AI project should accomplish and what it takes to build it.
Explore the Quantic MSAIE curriculum to see how its applied projects connect business needs with AI engineering and leadership.
FAQs
“The way it fits into real life.” After completing his Quantic Executive MBA in 2024, Luis returned for practical experience building AI systems and leading AI adoption at work. He valued being able to study in short sessions alongside his leadership role and family commitments, as well as coursework that connected engineering with business decisions.
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Luis built and deployed an HR policy assistant for a fictional company. It retrieves relevant information from 17 policy documents to inform its answers. His work included document retrieval, tool selection, testing, and deployment. He has shared the project on GitHub.
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Luis has started using an adapted course requirements template with his technical leads. Before sketching an architecture, he asks what decision an AI initiative should improve, what data is available, and what a small first version could look like. These questions help him clarify the request before beginning design work.
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Quantic’s MS in AI Engineering combines technical rigor with a curriculum accessible to a broad range of technology-motivated professionals. It develops the AI skills and business judgment needed to lead and implement AI initiatives. Its applied projects connect business challenges with AI solutions. The program also includes a Capstone focused on carrying out an end-to-end, real-world AI initiative.
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