I empathize. Been doing ML for a similar amount of time, and the amount of people who claim to know ML, but lack basics, abound. I don't think they are to blame (after all learning isn't bad, and everyone wants to capitalize on a market); we need to adapt to the fact that the definition of ML practitioner has considerably broadened: from someone who knows only to use vgg16 from PyTorch, without understanding the optimization, loss, alternative algorithms etc to someone with a much broader and deeper view of the area. Problems arise when people expect candidates of the latter type but instead end up running into a steady stream of candidates of the former type.

Hiring Managers probably need to get used to the fact the term "ML practitioner" alone doesn't mean much anymore, and they need to set expectations accordingly.