My name is Maikel Withagen, I’m 35 years old, and I’ve been working as an AI/ML engineer since November 2017. In August 2025, I joined Payt as a Senior Machine Learning Engineer — the first in that role.
Before this, I worked on a project basis and then at startups. Quick decision-making and having a lot of autonomy suited me well. However, tight budgets and constant time pressure often lead to quick fixes. You rarely get the chance to do things properly, and that started to bother me.
At Payt, things work differently. There are no unnecessary layers, and if a message suffices, there’s no meeting. Everyone takes on their work independently, and decisions are made directly. What gets built is done so because it benefits the customer — not just because the sprint is over.
The first few weeks were a great challenge. Every developer works independently on their own tickets — there’s little consultation, and everyone knows what they’re doing. As a new ML engineer without a ready-made task list, I had the freedom to find my own path. I had to get to know the application, figure out what ML functionality was already in place, what the future needs were, and most importantly: how to prioritize all of that. This freedom to set things up myself fits perfectly with how Payt operates.
My role has two aspects. I work on the product itself: expanding new features and existing functionality with AI. Additionally, it involves how the team engages with AI — in workflows, in how you tackle problems. The goal is not to replace people but to make the team work more efficiently. Payt wants to continue growing without becoming a large corporate company — that requires working smarter with the same team. AI can play a role in that. There’s definitely curiosity among colleagues.
You see it everywhere: companies trying to keep up with what AI has achieved in recent months. A new model every week, a new milestone every month. The challenge is not to keep up with that, but to determine what actually benefits your customer. That’s where Payt’s focus lies — not embracing the latest technology just because it’s possible, but building what works.
At the same time, it’s clear that the product itself will also change. AI advancements are rapid, and customers expect more flexibility — not just a software package that does what it does, but a service that adapts to their needs. The shift from a pure software product to SaaS is no longer a choice; it’s a direction you must take as a company. This requires different choices in how you develop, how you serve your customers, and how you handle change. That’s exactly the kind of challenge I want to contribute to in the coming time.
