You can do a lot of work on MLOps and get very far without knowing much about ML. In a team with a senior ML engineers you are helping them scale and build stuff.
Like say you want to generate tons of synthetic data using simulations, you are likely to be more interested in questions say of batching, encoding formats, data loading etc than the actual process of generating unbiased data sets
If you need to collect and sample data from crowd sourcing, you likely need to know less about reservoir sampling than say figure out how to do it, online so it's fast or be efficient with $$$/compute spent on implementing the solution etc.
Like say you want to generate tons of synthetic data using simulations, you are likely to be more interested in questions say of batching, encoding formats, data loading etc than the actual process of generating unbiased data sets
If you need to collect and sample data from crowd sourcing, you likely need to know less about reservoir sampling than say figure out how to do it, online so it's fast or be efficient with $$$/compute spent on implementing the solution etc.