The barriers to AI engineering are crumbling fast

Is that really AI engineering or Software engineering with AI?

If a model goes sideways how do you fix that? Could you find and fix flaws in the base model?

Agree that the use of "AI engineers" is confusing. Think this blog should use the term "engineering software with AI-integration" which is different from "AI engineering" (creating/designing AI models) and different from "engineering with AI" (using AI to assist in engineering)

I wonder if either could be really be called engineering.

The term AI engineer is now pretty well recognised in the field (https://www.latent.space/p/ai-engineer), and is very much not the same as an AI researcher (which would be involved in training and building new models). I'd expect an AI engineer to be primarily a software developer, but with an excellent understanding of how to implement, use and evaluate LLMs in a production environment, including skills like evaluation and fine-tuning. This is not some dataset you can just bundle in software developer.

You find issues when they surface during your actual use case (and by "smoke testing" around your real-world use case). You can often "fix" issues in the base model with additional training (supervised fine-tuning, reinforcement learning w/ DPO, etc).

There's a lot of tooling out there making this accessible to someone with a solid full-stack engineering background.

Training an LLM from scratch is a different beast, but that knowledge honestly isn't too practical for everyday engineers given even if you had the knowledge you wouldn't necessarily have the resources necessary to train a competitive model. Of course you could command a high salary working for the orgs who do have these resources! One caveat is there are orgs doing serious post-training even with unsupervised techniques to take a base model and reeaaaaaally bake in domain-specific knowledge/context. Honestly I wonder if even that is unaccessible to pull off. You get a lot of wiggle-room and margin for error when post-training a well-built base model because of transfer learning.