I think at least part of it is that many people creating the job postings literally don't know the difference between a machine learning specialist and an "AI E

I think at least part of it is that many people creating the job postings literally don't know the difference between a machine learning specialist and an "AI Engineer" who is just calling an API. One thing that is confusing is that with the powerful models that have come out in the last few years, someone using the right API and maybe a good prompt can actually handle many tasks that three years ago would have taken an ML engineer months to train a custom model for. I have been saying I'm a "software engineer with a recent focus on generative AI". Half of my clients are convinced that they need to train a custom model in order to solve their problems at all. Because they typed a request into ChatGPT and it didn't do exactly what they wanted. But usually if I just do an API call with an actual system message and temperature 0 then I can demonstrate that they don't need a custom model. There are good reasons to want custom models, but those are probably phase two or three of most projects. And 100 X as expensive and complex. Phase one for many projects will work fine as a proof of concept using an API call or two. I think there will be jobs for ML specialists that know how to do fine-tuning of common models. But you will be competing with people like me who don't have an ML PhD. But beyond that, it seems that you have to build specialized architectures that can compete with the general ML architectures built by leading edge researchers at huge companies. And it's becoming increasingly difficult to invent a new architecture that is truly better in a niche.