> It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases where AI/ML fails on a deceptively-simple problem, or to tweak an AI/ML algorithm (or develop a new one) so it can solve a novel problem.
How much of that is critical domain specific knowledge and how much of that is just general engineering debugging/problem solving experience though? Certainly the person who does have the masters/PhD and a few years of applying that to real-world ML problems will have the edge but an experienced developer who's got a knack for maths (though no direct ML experience) may be able to get up to speed quicker than you think. Part of that will be experience with knowing how and when to ask the right questions when you get stuck.