“What I’ve enjoyed most about Quantic’s MS in AI Engineering so far has been the hands-on learning, particularly the deep learning and AI model fine-tuning courses.” Krista Dotzenrod came to Quantic MSAIE with a master’s in software engineering and years of experience building backend systems. At Spotify, where she’s a senior software engineer, she works on tools that support content recommendations.
Krista wanted to understand how a model learns from data and what an engineer can do to adapt it to a particular task. Hands-on exercises in Google Colab gave her room to experiment with models herself. One of the first places she noticed a difference was in conversations at work. When colleagues discuss how an AI system is designed, Krista says she can follow the reasoning behind their choices and ask more informed questions about projects that once fell outside her expertise.
Quick Answer: Why is a backend engineer learning to fine-tune AI models?
“What drew me to Quantic’s MS in AI Engineering was the opportunity to go a level deeper and really understand how AI models work, how they’re trained, and how we can adapt them to solve specific problems.” Krista Dotzenrod brought years of backend engineering experience to her studies. She wanted to understand which choices an engineer can make during training and how to tell whether a model is getting better at its task.
- In her coursework, Krista experiments with machine learning models in Google Colab. She’s especially interested in how training data and settings affect a model’s performance.
- At work, she can follow the reasoning behind architectural decisions and ask more informed questions about AI projects that once fell outside her expertise.
- Looking ahead, Krista wants to build on what she learns in those conversations and help design, develop, and deploy AI systems herself.
How did Krista get from ad operations to backend engineering at Spotify?
“As a backend engineer, I’ve spent my career building complex systems, but AI introduces an entirely new set of technologies and approaches.”
Early in her career, Krista worked in digital advertising, checking that ads ran as planned and reached the intended audience. She later helped publishers troubleshoot advertising technology at Sovrn. By the time she joined Target as a senior software engineer, she was building tools for the kind of campaign work she knew firsthand. One of those tools let media strategists choose which audiences to reach with their digital ads.
Krista joined Spotify in 2021. Her work there has included developing tools for creators and helping curate podcast recommendations. Today, she works on tools that support recommendations across different types of content. With that experience and a master’s in computer software engineering from Regis University, Krista had a specific reason to pursue Quantic MSAIE. She wanted to understand how engineers can train a model and adapt its behavior to the problem they’re trying to solve.
Why pursue Quantic’s MS in AI Engineering after a software engineering master’s?
“When I first started, Quantic’s MS in AI Engineering program was newly launched, and I was excited by the opportunity to pursue a degree that felt so closely aligned with where the technology industry was heading.”
Years in software engineering had shown Krista how quickly the work could change. As she considered where to take her career next, she wanted to be able to design, build, and eventually deploy an AI system herself. She also saw those skills as a way to grow in her career, with the possibility of moving into a role focused on AI.
Quantic MSAIE prepares people in business and technical roles to lead AI projects that address a business need. Krista wanted to understand the engineering decisions behind that work. How do you train a model for a particular task, and how do you tell whether it does the job well? Those were the questions she especially enjoyed exploring in her deep learning and fine-tuning courses.
What did Krista learn about model behavior in Quantic MSAIE?
“It’s one thing to interact with an AI model, but it’s another to understand what’s happening behind the scenes and how you can actually influence its behavior.”
In her Google Colab exercises, Krista experimented with machine learning models and explored how neural networks learn from examples. She especially enjoyed studying fine-tuning, where an engineer takes a model that has already been trained and trains it further on examples chosen for a particular task.
What interested Krista was how much the engineer’s choices matter. The training examples shape what a model can learn. A setting called the learning rate, for instance, controls how large an adjustment the model makes at each step. Testing it on examples held back from training helps an engineer judge whether those adjustments have made it better at the task.
For Krista, working with PyTorch, NumPy, and scikit-learn was a chance to put those ideas into practice. That experience began to carry over to work, where she could follow more of the reasoning when colleagues discussed how to design an AI system.
What can Krista now follow in an AI architecture discussion?
“I’m better equipped to understand architectural decisions, ask more informed questions, and follow along with projects and workstreams that previously fell outside my area of expertise.”
What struck Krista was how quickly her coursework became useful at work. When a discussion moves beyond the backend systems she knows well, she now has her own experience experimenting with models to draw on. Choices about training data and how to judge a model’s performance are familiar from her Quantic MSAIE exercises.
Being able to take part more fully in those conversations already matters to Krista. “For me, that’s already a meaningful return on the investment.” In time, she hopes to help design the kinds of AI systems she’s learning to discuss with her colleagues.
How does Krista make room for graduate study alongside full-time engineering work?
“The lessons are easy to incorporate into my daily routine, while the deadlines and milestones keep me motivated and on track.”
Krista can choose when to study around her full-time role at Spotify. She also values being able to work through a difficult concept at her own pace. She finds the interactive lessons engaging even when the material gets complex, and she likes having a deadline to work toward. It gives her the accountability she wants from graduate school while leaving her room to decide how studying fits into her day.
“I love that I can continue challenging myself academically and growing professionally without having to put the rest of my life on hold.” One part of her Quantic MSAIE experience did take her by surprise: how many friends she would make through an online degree.
What did Krista find at Quantic’s London and Vancouver conferences?
“Quantic does a great job of bringing people together through events and global meetups,” Krista says. She especially enjoyed attending the London and Vancouver conferences.
What stayed with Krista after Quantic’s in-person events was how easy it felt to talk with people. Whether she was speaking with a peer from her cohort or meeting an alum for the first time, she found people willing to share their experiences. She especially valued hearing from people whose industries and career paths were different from her own.
Those connections now come to mind when she travels. “It feels like no matter what city I’m visiting, there’s an opportunity to connect with fellow students or alumni.” For Krista, some of the most unexpected rewards of Quantic MSAIE have been personal. She expects the friendships she’s made to stay with her long after graduation.
Where Krista wants to take her AI engineering skills next
“I’m excited to continue building on that foundation and move from participating in these conversations to actively contributing to the design, development, and deployment of AI-powered solutions.”
At Target, Krista helped build a tool for media strategists who needed to choose an audience for a campaign. Now she wants to bring that same practical focus to AI engineering. Given a problem worth solving, how could she adapt a model to address it, and how would she know it worked?
Krista can already see progress in the questions she asks at work. When colleagues discuss the design of an AI system, she can draw on her own experience with model training and evaluation to follow their reasoning. As she continues her studies, she hopes to help make those design choices herself.
Explore the Quantic MSAIE curriculum to see where model training, evaluation, and implementation fit into your own learning goals.
FAQs
“I feel like I found Quantic at exactly the right time in my career, and I’m excited to keep learning and working my way through the curriculum!” As a senior software engineer at Spotify, Krista had seen how quickly her field could change. She wanted hands-on experience building AI systems and saw that as a way to grow in her career, including the possibility of moving into an AI-focused role.
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Krista used Google Colab to experiment with machine learning models and explore how fine-tuning works. She also gained experience with PyTorch, NumPy, and scikit-learn. She particularly enjoyed learning how the choice of training data and settings can affect a model’s performance.
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Krista says she can follow AI projects that once fell outside her backend engineering specialty. When colleagues discuss how to design a system, she can better understand the reasons behind their choices and ask more informed questions. She hopes to help make those design decisions herself as she gains experience.
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Quantic’s MS in AI Engineering prepares professionals in business and technical roles to lead AI initiatives and put them into practice. The Quantic MSAIE capstone project gives students a concrete example. Working as a team, they design, build, test, and deploy an AI application or agent, then demonstrate to project stakeholders how it meets the agreed requirements.
More Student Spotlight:
- Putting AI Into Practice: Justin Wright’s Quantic MSAIE Journey
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- Leading AI Adoption From the Front: Warren Rehman’s Quantic MSAIE Journey
- Bridging AI and Business: Christopher Arnold’s Quantic MSAIE Journey
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