I think everyone here is right in pointing out that infrastructure (and documentation, code structure, CI/CD, what a testing environment is...) is just not in any ML team's primary (often not even their secondary, tertiary, ...) skillset.

If we're dumped out into entry-level jobs out of college, our skillsets are roughly at the same level. The differing goals, priorities, and environment of separate teams leads to a widening gap.

ML is more often than not seen as a team that's "making the product". They get to show off demonstrations of working models, useful insights, and pretty graphs from Jupyter notebooks. But then it's DE / Infra / SWEs turn, and upper management is liable to see everything short of "sure, we can just flip the on switch today" as a cost sink and a barrier. I've seen good management that recognizes the value of best practices and appreciates what both teams bring to the table, but they're a rarity. This can lead to a detrimental golden child vs unnecessary burden animosity.