Those are ML and AI frameworks that use a tremendous amount of mathematics under the hood, but you can also reliably treat them as blackbox learning systems too. Understanding the model generation procedure and setup is often unneeded. And many tools will help direct you toward what algorithms makes the most sense for your data, and even have competitions to figure out which actually works best. I agree, it's a little disappointing, but admittedly it doesn't take a PhD to do this stuff anymore.

It is important to note that just because you can do all the stuff a PhD Scientist might regularly do, doesn't mean that someone will hire you for it. In that case you might need to have a PhD in mathematics, computer science or a related field. But that is more a consequence of competition and long term talent investment, than the practice of ML/AI itself.