AI problems turn into data problems. The happiest and best compensated people I know in that area have gone into data engineering, because data engineers are the ones selling shovels in this gold rush.
Do you mean collecting, cleaning data? Or setting up databases (if yes, how is it different from me managing my employer's databases, except for size)?
In other words, what does a data engineer do all day?
Network and meet someone who will give you, a web developer (a profession that is heavily stigmatized in the industry), a chance in a very competitive field.
People who can create beautiful and user-friendly interfaces are extremely valuable and respected.
But my experiences trying to set up a typescript project and the unbelievable number of frameworks and crazy things to navigate (mjs/cjs) made me yearn for the pains of the Python ecosystem again.
In the Hollywood example from what I know of.. small "tiger teams" assemble with fundamentals, then quickly farm out the sexy work to disposable contracting firms, who then hire even more disposable people with various skill levels. In other words, lots of fun and excitement but also lots of work place abuses and low stability. Over time, Hollywood formed unions for a surprisingly large number of roles (like writers) because the real truth of business is not pretty. Needless to say, Silicon Valley has moved very quickly, and the Hollywood stories are not exactly applicable.
This comes from someone doing web dev after studying data science, so I don't know how well it reflects reality as I've never worked as a Data Eng. myself
I wouldn't label it that way, AI/ML has its own ops and devops which is very distinct from data engineering.