How to Become a Top AI Engineer: 3 Essential Qualities

Mark Hallenbeck, senior lecturer for UIC’s Online Master of Engineering with a concentration in AI and Machine Learning program and director of the Caterpillar Inc. Innovation Lab at the UIC Innovation Center, has spent years helping students and companies use artificial intelligence to solve complex problems. In his work with corporate partners such as Caterpillar, BMW, 10G LLC, and OSF HealthCare, Hallenbeck has seen firsthand what separates a good AI engineer from a great one.

Here are the three qualities he believes define the most successful AI and machine learning engineers; insights that can help anyone preparing for AI engineer jobs today.

1. Understanding the human side of data

The first quality is the ability to understand the human side of data. Hallenbeck explains that successful AI engineers must recognize that digital activity represents human intention, not just numbers on a screen. This approach, called human-centered data science, emphasizes the people and experiences behind the data.

“How people shop for household goods on Amazon is very different than how people shop for construction equipment on a Caterpillar website,” he said. “On the surface, it may look like the same digital behavior, but you will be wrong if you interpret it through the wrong lens. Because you don’t understand the motivations and what they’re trying to do that drove that data.”

“It’s not enough to just take terabytes of data, put it in a supercomputer, and get an answer,” he added. “That digital activity represents intention. Understanding the nature and context of the data you’re working with is one of those things that separates the really good AI engineer from the rest.”

2. Scaling solutions to fit real-world business problems

The second quality is the ability to scale solutions appropriately. It’s not enough to design an elegant algorithm. AI engineers must consider business realities such as budget, available data, team capacity, and long-term maintenance.

“Not every company has access to a supercomputer. Not every company can generate all the data needed to build a classification system,” Hallenbeck explained. “An excellent AI and machine learning engineer understands those factors and adjusts the solution to fit the business context. That’s what makes their work effective and impactful in AI innovation.”

3. Defining the real problem and communicating solutions clearly

The third quality is defining problems accurately and communicating solutions clearly. Too often, organizations want to apply AI to everything, but the best engineers take time to identify the actual problem and the level of AI required.

“Too often, people want to throw AI at everything, but the really good engineer will figure out what the real problem is and what level of AI fits the situation. Sometimes you’re using a sledgehammer where a thumbtack would do,” Hallenbeck said.

“It’s not enough to be a brilliant AI engineer; you must also be an excellent communicator,” he emphasized. “The very successful AI and machine learning engineer will also be able to explain the importance of what they’re doing to people who aren’t AI engineers.” Engineering doesn’t work in a bubble. AI professionals need to gain the trust of business stakeholders who may not have technical expertise. If engineers can’t explain their solutions in clear, convincing terms, those solutions won’t be used.

Preparing for AI engineer jobs with a Master of Engineering degree

Through his teaching in UIC’s MEng degree program, Hallenbeck brings these qualities into the classroom. He uses real-world stories from his work with industry partners to help students see how problems unfold and what decisions engineers make along the way.

“Innovation work requires both technical ability and the ability to explain and justify your ideas. In my classes, I walk students through examples, critique their work, and push them to ask deeper questions. That’s how they start building these qualities, not just as engineers, but as problem solvers who can make an impact.”

Students in UIC’s Online Master of Engineering degree with a concentration in AI and Machine Learning gain both the technical skills and the broader perspective needed to apply AI in engineering. By learning directly from experts like Hallenbeck, they develop the exact qualities that set apart the best AI engineers in today’s rapidly evolving field of AI innovation.