AI Engineering is broad. There are a lot of things to learn and a lot of mistakes you have to made yourself.
For applied ML, my tips are: make sure you learn the dark side of BatchNorm and Dropout, start with simple and elegant baselines instead of complex SOTA algorithms, spend more time on understanding your data than trying algorithms, be aware that SOTA in a related task will often suck at your task, be data driven. Also, most of your ideas will not work but you have to try and conduct experiments carefully.
For applied ML, my tips are: make sure you learn the dark side of BatchNorm and Dropout, start with simple and elegant baselines instead of complex SOTA algorithms, spend more time on understanding your data than trying algorithms, be aware that SOTA in a related task will often suck at your task, be data driven. Also, most of your ideas will not work but you have to try and conduct experiments carefully.