A lot of the people on this forum are from a quant background so can't definitively answer your question. Based on experience of switching between different fields I'd say try to make the AI work look as little like Plan B as possible both in how you write your CV and what courses you do (doing financial work in your masters is very distracting to employers and this isn't the 1950s where employers couldn't care less if you took a job as a 'gap job').
It's about having skills and how you present them - the question might get a lot is "why did you do MFE then midway decide to do AI?". Whatever answer you give it has to be your own and ensure employers feel you won't get distracted by another matematical area in another 6 months time. HR will ask and I wouldn't really worry about, they're usually twats and get overruled, but your prospective line manager will probably ask this.
First thing is do a thesis in AI, but I'd also do as much research and work outside of coursework. At the very least do programming of your own (quant employers, including the one that hired me, were very impressed when I said I'd programmed basic games in C++ - this was also as I'd learnt a lot of libraries even a PhD wouldn't necessarily know). Your thesis doesn't have the conditions of work experience - internships cost time, but also you can grab a public dataset and work on it, publish a blog and the code goes on GitHub. What employers bitch about a lot is when grads come in and can't hack the real world problems because they don't like the data wrangling (possibly 90% of any job involving data analysis or algorithms. I mean I love it and while the actual sexy work is 5% for me it's 95% sexy... Nice...) or using stupidly out of date systems. You won't replicate all of these conditions, but you can go beyond the confines of academia and can go some way to bridging the gap between academia and work.
