I've recently been tasked with hiring new college grads (less than 2 years of experience) AI Engineers. Some things that we've been doing:

1) Do they understand the ecosystem of algorithms and models? How do those coexist? Statistical models are the right choice sometimes. Sometimes it's XGBoosted RFs, sometimes NNs, LLMs, etc. And they're not mutually exclusive. I don't think that has changed since AI to be honest - though I get bad candidates that say LLM-everything of course.

2) AI-assisted fluency. Not just in coding, but in concept build. I don't expect them to have the velocity of an AI-fluent principal engineer, but I want to see that they're not resistant to AI-assistance. This is obviously new.

3) Experience with production systems, more than before. Pre-AI, I'd accept that a recent grad might be tuned towards models and algorithms, and wouldn't know much about frontend or backend, or anything you run into in production environments. Given the ease with which you can now setup a small DB, your modeling pipeline, and a full react dashboard or fastAPI frontend...I'd at least like to see they've dabbled in all of that, have a rough sense of it. I don't need them to be full-stack, or even comfortable with it - but AI has raised the breadth bar for me.