AI does help an engineer who embarks on a new voyage through unfamiliar APIs to guide them with usage patterns, but some people become much more efficient by going through the library docs.
Typing out the code is the smallest part of a "good engineer's" job (and even so, having to adapt most of AI generated code is slower than typing it out yourself once you do understand the APIs).
I do think it might work well for MVP-style quick prototyping, but using this as an applicant qualification criteria seems so weird (even when building an MVP, you want some tension between building it quickly and building it the right way).
Even supposing that is true, what if you have a choice between a bad engineer who uses AI and a good engineer that won't?
AIs generate deeply mediocre code. This is better than anything a person who can't code on their own would produce, but an experienced developer will have to spend all their time babysitting the AI to get it to behave properly.
Would you rather write code yourself, or ask a first-year student to write it for you while you watch over their shoulder and tell them to go back and try again every time you notice a mistake? Which of these do you think is faster and better in the long run for the quality of your codebase?
I am currently in the process of hiring a backend engineer. Anybody who does not use AI to aid development work gets an automatic disqualification. In my experience, a good engineer using AI tools will run circles around a good engineer not using AI tools.