I would say you do machine learning if you're doing optimization on bespoke models. These worlds blend together once you get into the weeds. It doesn't have to be gradient descent (via AD or otherwise) to be machine learning.

By "light to moderate amount" do you mean that you haven't catalogued the menagerie of statistical models that are commonly taught in machine learning / deep learning courses? Because that to me is secondary to having the underlying principles (optimization theory, measure theory, etc.) down pat. Recognizing the fundamentals in the theory which describes how machine learning proceeds is invaluable for comprehension.

If you wanted a flashier name for your role I might suggest "Solutions Architect" or even "Machine Learning Engineer" based on what roles you want to aim for. Because honestly you're doing a lot of what is already entailed by those titles. "Data Scientist" also fits for sure, being such a broad title nowadays.

I interpreted what you said more glibly(?) than it seems you intended, and apparently I expressed my surprise snarkier than I intended.