I'm a DS with ~10 yoe mostly in classical ML and I've been wondering how much/how fast I need to pick up "high level" LLM skills. I've seen the way the winds are blowing wrt to the DS job title and so have been moving towards more of engineering roles. But it seems like every job posting right now for ML engineering positions is asking for "pytorch, DL, LLM" experience, even for companies where getting into the nuts and bolts of inference would not really be a core value prop. Like I really find it hard to believe that financial/insurance companies (my primary domain) are building and fine tuning their own models for chat/RAG applications or whatever (or if they are, should they be?). I haven't really seen huge value add from even the most sophisticated RAG setups yet.
I use LLMs every day for code assistance so I'm not totally naive to the whole thing, but I seriously wonder what direction to take my career, exactly. Should I be going all-in on learning CUDA? Is knowing how to wire up a few API calls enough for "soft" applications? Should I in fact be keeping up with the latest RAG techniques (which seem to change every time a new foundational model gets released)? Should I just stay out of the whole thing and double down on classical ML applications in my domain?
I use LLMs every day for code assistance so I'm not totally naive to the whole thing, but I seriously wonder what direction to take my career, exactly. Should I be going all-in on learning CUDA? Is knowing how to wire up a few API calls enough for "soft" applications? Should I in fact be keeping up with the latest RAG techniques (which seem to change every time a new foundational model gets released)? Should I just stay out of the whole thing and double down on classical ML applications in my domain?