How This Software Engineer Became a Machine Learning Specialist with UIC’s MEng Degree

Crista Mondragon is a proud UIC alum with a background in computer science and several years of experience in software engineering. While working in the conversational AI space, she began to develop a growing interest in the machine learning technologies behind virtual assistants. To build on that interest and advance her career, she enrolled in UIC’s Online Master of Engineering (MEng) with a concentration in AI and Machine Learning. The degree helped her strengthen her technical expertise and ultimately transition into a new role as a machine learning specialist at Bank of America.

In this spotlight, Crista shares how the MEng degree helped her shift into a more specialized AI career, deepen her understanding of natural language processing models, and stay ahead of the curve in a rapidly evolving field.

Can You Provide a Brief Overview of Your Background?

I have a bachelor’s degree in Computer Science from UIC, so I’m a proud UIC alum. For the past five years, I’ve worked in the conversational AI space, primarily focused on building and improving chatbots and virtual assistants from a software engineering perspective. Before my current role, I worked at AT&T as an AI software engineer on their Smart Home Manager virtual assistant. That experience deepened my interest in conversational AI.

After graduating from the MEng program, I transitioned from software engineering to a specialized role as a cognitive linguist, also known as a machine learning intent recognition specialist. I now work at Bank of America, focusing more on the machine learning side of virtual assistants rather than software engineering. This change has allowed me to dive deeper into AI and natural language understanding.

Career Advantages Gained from Earning the Online MEng Degree

Earning my Online Master of Engineering degree at UIC played a key role in helping me make a successful career transition from software engineering to machine learning specialist. During interviews, I could confidently highlight my experience in conversational AI and the deep machine learning skills I gained through the program. This combination gave me the credibility to say, “I understand these technologies and have the technical background to excel in this role.” It’s how I landed my current position as essentially a natural language processing engineer.

The course MENG 416: Natural Language Processing introduced me to several NLP algorithms that I continue to apply daily in my work to improve intent recognition strategies. Overall, the MEng degree gave me a technical edge, opened new job opportunities, and helped me position myself for career growth.

What Led You to Switch from Software Engineer to Machine Learning Engineer?

Honestly, I started to feel a little bored with software engineering. I enjoyed it, but I wanted to explore something new after a while. Since I was already working in the conversational AI space, I felt more confident moving into machine learning after gaining new skills through the MEng program.

The two roles are definitely different. Software engineering is more about building products, including writing a lot of code, following development processes, and delivering something final. Machine learning, on the other hand, is more focused on working with data. The coding I do now is about creating tools that help improve our models, rather than building a complete product from start to finish. It’s also a more continuous process. In my current role, for example, we’re always adding new intents for our virtual assistant to recognize, so the work is ongoing and evolving. The dynamic nature of machine learning really keeps things interesting for me.

How Did the Program Help Prepare You for Emerging Trends in Engineering?

I’ve been working in the conversational AI space for about five years, so I came into the program with some background in the field. The machine learning and deep learning courses helped me build on that general AI knowledge in a much deeper, more technical way.

These courses focused more on the algorithms and practical applications behind the technology, which gave me a clearer sense of where the field is now and where it’s headed. For example, in MENG 416: Intro to Machine Learning, you start with foundational concepts, and in MENG 417: Intro to Deep Neural Networks, you begin exploring more advanced models that are constantly evolving. The resources in the program also helped prepare me to understand and work with newer, more complex algorithms as they emerge. A good example is ChatGPT. Many people use it, but few understand how it works. These courses gave me insight into the backbone of that kind of technology, which has been extremely valuable.

The Importance of Legal and Ethical Training in AI and Machine Learning

MENG 400: Engineering Law was one of the most eye-opening parts of the program. Many engineering students tend to overlook legal and ethical aspects since they seem more humanities-based, but in AI, they’re crucial. For example, I often have to create model reports documenting all our changes. Understanding the laws and regulations helps ensure we stay compliant and avoid unintentional violations.

Ethics, in particular, is a vast area that people don’t always fully grasp. Data bias is a significant issue; it can impact an entire model without us even realizing it. In my voice recognition and transcription work, accents play an essential role. If the person testing the system has an American accent, but many users have Indian accents, the system might struggle to recognize them accurately. That kind of bias affects performance and user experience. This is why it’s so important to include diverse populations in testing and training data to ensure AI systems work fairly for everyone. The program helped me understand why this matters and how to approach it responsibly.

Most Valuable Project: Building a Deep Learning Model with Real-World Data

We had a final project in MENG 412: Intro to Machine Learning, where we built our own model and wrote a formal report about it, which I found especially valuable. Throughout the project, we applied different algorithms, iterated on our models, and compared results to determine what worked best. That kind of hands-on experimentation mirrors what you’d do in a real-world role, starting with a blank slate, testing approaches, and making data-driven decisions. We also had to format our final report using the structure of an engineering paper, which was a great way to build communication skills in a technical context.

The project was open-ended so that we could choose our own topic. I decided to work on music genres and used a Spotify dataset with features like pitch, tempo, valence, and energy. Their API was really useful and gave me a lot to experiment with. I was able to see how different attributes might influence genre classification.

What Was Your Experience Like With UIC’s Online MEng Format?

I worked full-time while completing the MEng degree, so balancing everything took discipline and strong time management. One thing that helped was setting aside at least one day each week just to relax and recharge. The flexibility of the online format made a huge difference, though. I didn’t have to worry about commuting, which saved time and reduced stress. I even spent part of a semester in Hawaii doing a few hours of homework each day and still enjoying the beach. That kind of flexibility just isn’t possible with an in-person program.

Even though the program was online, it felt very connected. Weekly office hours with professors were invaluable. They were responsive, explained things clearly, and sometimes gave mini-lectures. I also appreciated the discussion forums and group projects, which allowed me to collaborate with classmates from different industries, like medical devices and construction. Working with people from various backgrounds gave me a broader perspective and helped me build my teamwork and communication skills in a way similar to a real work environment.