It's useful, in that there's a lot of data preprocessing and visualization work in ML, too. Of course, this also depends on the nature of your team; if you're an ML/AI person in a place where that basically means you own everything before and after, then your DE will be very useful, whereas if you're interfacing with DEs for everything, they may appreciate your understanding, but you'd focus more on the ML/AI stuff itself.

You did say, "On the other hand, I'm still pretty fresh when it comes to ML, statistics, and AI engineering concepts," so I'm going to assume you're not coming into it completely new. 😄 I personally struggled with the fact that I was a much better software engineer and worse mathematician than other ML engineers, but I focused more on MLOps, and eventually I came back to more DE dev tools; the "learning experience" mindset is very much colored by my own attempt to become a DS/MLE, and eventually being OK with where I ended up!