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As someone who hires a lot of ml engineers a masters can be a good way to learn but isn't a strong signal on a resume.

Hands on experience and side projects (especially ones in investing/betting where you actually put money on the line) count for way more.

A good masters will help you nail down the fundamentals of linear regression, metrics, regularization, gradient descent, metrics etc. That knowledge doesn't go out of date and you do use it and get asked about it in interviews.

Large language models etc are growing rapidly but how most practitioners user them is much slower. People still use BERT and the smaller ones for computational reasons and ways you fine tune, evaluate, prompt engineer etc for the larger ones don't change that quickly.

There is also the large area of debugging, monitoring, performance tuning and improving large training and production systems. Even if generative models write could write great code for an entire system i don't see this area going away any time soon.