You got it. It's unbelievably difficult to get model devs out of the mindset of training on their own VM, saving model outputs and metrics dumps to arcanely named file shares, etc. Once you can convince them that using stuff like workflow pipelining tools and centralized model repo servers isn't going to impede their creative process and that it prevents the mad scramble to find artifacts when there's turnover on the team, things become much more efficient.

Pretty much this. "ML engineering" has come to refer to the somewhat specialized task of implementing models and algorithms, and "ML ops" has come to refer to all of the other stuff that you just mentioned.

Don't forget "keeping the train data from leaking into the test data" and "having a way to reproduce the exact same model I trained last week". Those two are too often forgotten.