> So is the "engineering" part of this finessing the input and massaging the output? I don't know if I'll ever use the phrase "AI Engineer" myself, but there's

> So is the "engineering" part of this finessing the input and massaging the output? I don't know if I'll ever use the phrase "AI Engineer" myself, but there's plenty of meaningful engineering work in that space that strays pretty far from just calling some provider's APIs. A few that come to mind just for LLMs: - Custom fine-tuning of foundational models both in the classic sense and with more modern strategies like PEFT/QLoRA - Data preprocessing pipelines to help automate fine-tuning, vectorization, etc - Continuous integration suites to evaluate models on standard benchmarks as they change over time - Vector db / semantic search engineering to help decorate context windows effectively - Architecting ensemble models infrastructure to accommodate more complex task processing I think many of those probably going into what folks are calling the "MLOps" bucket, but I think its a more broadly a combination of research, application engineering, and operations engineering. Edit: for clarity, my position is that the line in the article between AI Engineer and ML Engineer need not be that bright. Just like software engineers today that write/operate their own devops tooling to deploy and manage the apps they build.