Are AI Engineers becoming mere "tool operators," or is self-reliance still the foundation?

I’m curious about the current landscape of AI engineering. In an era where AI-assisted coding tools are everywhere, do you think professional AI engineers still prioritize deep, self-reliant fundamental knowledge, or has the workflow shifted towards primarily leveraging existing tools and models?

Is "knowing how to code from scratch" still as crucial as it was a few years ago, or is the value now primarily in knowing how to integrate and optimize these powerful tools effectively? Would love to hear your thoughts on how this is shaping junior vs. senior roles