This is the right question. There are relatively few people doing "Capital A Capital I" work building and training generative language/image models. Especially now that people have realized that calling the OpenAI API is going to work better than dedicating a team to tuning your own BERT model.

So some options are: * Building ML models for traditional applications - forecasting, ranking, recommending, all of that. Data Scientists and ML Engineers haven't gone away. * Working as an "AI Engineer" building on top of existing APIs and models. Very hip, but also in flux - I couldn't tell you whether that role will still be considered rare & valuable in a few years time, or what skills will be core to it. * ML Ops, engineering work for building & serving AI & ML models. It's always good to be selling shovels.

I would try to get into a team you're interested in as a SWE and then upskill or pivot. In my experience this is more effective than trying to completely reskill and sell yourself as an unproven prospect for MLE or AI Scientist work. AI/ML teams still need software, and in fact many of the best researchers are not great software engineers.