[acidbaseextract]

I overall agree with the sentiment on delivery and needing to deal with a variety of issues, but one nit:

If it were my business or my team I would want an ML Engineer to relentlessly knock down any barriers to getting models into production.

My favorite is when they knock down the question of whether a certain project even needs ML to focus on getting ML into production.

Many teams fail at ML because there was an essential task that nobody wanted to do that didn't get done.

Many teams fail at ML because there was a task that didn't need ML (or at least anything more than a linear model) that is made opaque to anyone but an ML engineer after they implement it.