Some people are promoting the title "AI Engineer", which I think a sensible title... kind of like "full stack developer", not defined by a single technology, but requires knowing a number of technologies (LLMs especially) and being able to put those together into a working product.

Having worked with LLMs a lot I would NOT agree with people who call them "just another tool". Some people are experts working with cryptography, or creating scalable distributed systems, or working on network protocols, etc... these are real and specialized skills. Working with LLMs is similar. Starting from a foundation of "can get stuff working" (full stack) is awfully important, and I don't think LLMs reward narrow specialization.

Also prompt engineering is real, and I don't think it's going anywhere. Working in collaboration with domain experts to do that prompting is essential, but a lot of output issues benefit from a combination of both prompting and changes to pipeline, text processing, and other code-based approaches. There's real benefit to being good with words, a close reader, able to get in the head of the LLM, learning its fixations and misconceptions, templating thought processes, etc. Ideas that prompt engineering will disappear with better models and fine tuning are, IMHO, naive and misunderstand the interplay of prompt and LLM. If you want to get something from an LLM you will still need to know how to ask!