What Machine Learning skills should I be learning now to set myself up for success in the coming years?

Strong understating of the fundamentals - the ML concepts and algorithms, and the underlying math:

How Forward feed and backwards prop work.
The various loss functions and their considerations
The various activation functions and why they are needed
Optimization functions and why they are needed
Bias and variance / over and under fitting - what causes them, and the various methods to handle them
CNNs, RNNs, GANs, attention, Transformer, unsupervised and semi supervised, RL, decision trees, Ensemble Learning, SVM, Auto encoders…
Understand interpretation, bias, fairness
The statistics theory (the more the better), and the linear algebra and calculus technicalities

I highly recommend the "Neural Networks For Machine Leaning" course from University of Toronto, given by Geoffrey Hinton. It's a bit out dated in some not-so-meaningful sense, and definitely much harder than any other ML course out there. But if you survive through it, it provides deep mathematical intuition into ML, like no other course does.

It's a lot and not very easy, but if you do it - it will pay off. The libraries, frameworks, and hopefully also the concepts and algorithms will change over time. But if you have a solid understanding of the above, it will be very easy for you to keep up with the developments, grow, and adapt.