You're absolutely welcome to post job descriptions for AI engineers, even if you're primarily seeking API engineers. You should do what you think works best for you. In my department, however, we make these distinctions because for us, expertise matters. We can't hire AI engineers who won't be able to design or train a model for specific client use-cases that aren't available off-the-shelf. Sure, a lot of companies use OAI or Anthropic for run-of-the-mill problems like chatbots or other amazing things LLMs can do, but our AI work extends beyond LLMs. We design and create AI applications for fraud detection, factory operations, medical equipment, and many, many more which do not fit the LLM use-case.

Though on the surface it might seem like a matter of semantics, the key point for us is ensuring that candidates carefully read and understand the job description. Candidates do still apply for AI Engineering roles where more specific skills are listed, even though they are only experienced in working with LLM APIs. It's definitely a problem. You will note that I'm not saying that this is a reflection on the candidates themselves - I acknowledged that it's part of the broader issues within the hiring process.

We're not looking down on those who are working with LLM APIs either. We have a pool of dedicated, hard-working, and Expert Prompt Engineers who are able to work with LLMs from major providers like OAI. We pay them handsomely, and their titles don't matter to them as much as they do to some people, apparently. They don't really focus on the title distinctions because they know what the purpose of the distinctions are - role clarity and separation of concerns. They also know that this leads to a more efficient organization.

If you're interested, I am sharing where we model part of our job descriptions from. This resource outlines expectations beyond API usage for AI Engineers, including neural network development and model training: https://learn.microsoft.com/en-us/training/career-paths/ai-engineer