Hi, I am working in making the term "llm developer" more popular in France and train people to this new job.
We will need a bunch of them in the months/years to come to implement advanced AI systems after companies manage to properly pick and set up their AI platforms. Currently people would tend to involve data scientists into this job, but data scientists are often less versed into the software engineering aspect, eg when they work more on notebooks than web apps.
The job is akin to being a web developer, so a "normal" developer but specialized in a certain field. Knowing the internals of LLMs is a big bonus, but you can start your journey with treating them as black box tools and still craft relevant solutions. You'll need to learn about running systems with databases (vector, graph, relational and nosql are all useful) and plugging multiple services together (docker, kubernetes, cloud hosting).
Isn't this what originally data engineers were supposed to be? I get that the role has probably become "clicks buttons in the Azure GUI", but the "good" ones are basically backend developers that specialize on data stacks.
The data scientist roles have had a similar drift in my experience. They used to be "statistician who can code" or "developer who knows some stats", what we got is "clicks buttons in the Azure GUI".
I guess for the "backend" part it may be true, but there is a new "frontend" skillset of being able to actually implement a complex agentic workflow, and put that into a functioning product, which can be a lot of code. So closer to web dev in the end.
I have nothing against button clicking if it does work, as it may be a sign of maturity for a technical field. I click on a button in my car and turn a wheel and it usually go where I want, feels great. The equivalent would be the Microsoft ecosystem here.
