AI Engineering has become one of the most talked-about roles in technology today. But even with all the attention, many professionals are still unclear about what the work actually looks like on a day-to-day basis.
Is it mostly building models? Doing research? Writing prompts? Or is it something different once you’re actually in the role?
To help unpack this and clarify any doubts surrounding this career, we spoke with Agnes Wonder Okero, an AI Engineer, Machine Learning Engineer, Data Scientist, and Python expert working on production AI systems and automation.
AI Engineering is more than building models
If you think AI engineering is just building models all day, you aren’t alone, but you might be mistaken. Agnes points out that actual model training is only a small fraction of the work.
“AI engineering is much closer to systems engineering than many people realize,” Agnes explains.
A typical week for Agnes looks like this:
- 40% Engineering and Infrastructure: Building the “pipes” like APIs, Docker containers, and cloud deployment pipelines.
- 30% AI/Model Work: Retraining models and improving performance based on business shifts.
- 20% Data Issues and Debugging: Investigating why pipelines failed or why data suddenly looks different.
- 10% Communication: Aligning technical solutions with real-world business objectives.
What AI Engineering looks like day to day
For Agnes, the role is deeply operational. Much of the work is about making sure AI systems keep working correctly long after they are launched. This includes data cleaning and validation, monitoring, and problem-solving.
A significant amount of time is spent checking if data is arriving correctly, if it has missing values, or if it has drifted statistically, which can quietly break downstream logic and how the model behaves.
Most of the work, therefore, is about staying close to the system and understanding how it behaves in real time.
The Most Underrated Skill in AI Engineering
When asked what engineers underestimate most, Agnes points to Systems Thinking.
Many people focus heavily on machine learning models while overlooking software architecture, scalability, observability, reliability, and production infrastructure.
“A model that performs well in a notebook but fails in production is not a successful AI system,” Agnes notes.
This is one of the biggest mindset shifts for professionals moving into AI Engineering. The role requires understanding how models behave inside real systems when real users are using them.
The Reality of Production AI Systems
In movies, when a system fails, sirens go off. In AI Engineering, it’s rarely a dramatic crash, but usually, just a subtle drop in quality.
Agnes recalls a time when a model’s performance began to slip even though every dashboard looked green. The code was fine, the servers were healthy, but the users had changed. A product update shifted how people interacted with the system, making the model’s old assumptions outdated.
This is what engineers call Model Drift. “The world changes continuously, including customer behavior, markets, and even language,” Agnes says. “Good AI systems are not ‘deploy once and forget.‘ They require ongoing maintenance.”
Her biggest lesson is unlearning the idea that complexity equals value. A simple, well-engineered solution that is easy to maintain will always outperform a “cutting-edge” system that is unstable in production.
Can Software Engineers and Data Scientists Transition Into AI Engineering?
According to Agnes, the transition is very realistic for both:
- Backend Engineers: You already understand APIs and scalability. You just need to strengthen your statistics and data intuition.
- Data Scientists: You have the analytical skills, but you’ll need to learn more about deployment, DevOps, and cloud engineering.
“The strongest AI engineers are usually excellent engineers first,” she emphasizes.
Build the Future of AI with Moringa School
If you’re on the fence about joining this field, Agnes has one piece of advice: Focus on the fundamentals. The tools and frameworks will change every few months, but the ability to think critically and build reliable systems will always matter.
“AI engineering is one of the most exciting fields of our generation,” she says, “but the future belongs to those who can bridge multiple worlds: engineering, data, business, and human understanding.”
If you are a developer or data professional ready to move into AI, Moringa’s Applied AI Engineering course is designed for you. Learn how to:
- Integrate Large Language Models (LLMs) into apps.
- Build and deploy scalable AI workflows.
- Work with real-world infrastructure and modern tools.
Explore the Applied AI Engineering Course at Moringa School and start building the future today.
