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.
Indeed! My job involves approximately zero machine learning, at least for a narrow or stereotypical definition of machine learning. I work on optimization, domain-specific models inspired by queueing theory, various phsyically-motivated structural models, writing production code for data-heavy products, testing hypotheses in data, brainstorming how existing data can be used to solve new customer problems, writing documentation, communicating with customers, etc.
If you think this isn't data science but know of better nomenclature: please share! I've struggled to write a great job posting for this kind of work, and surely improved nomenclature would help :)