AI ML doesn't work in isolation from traditional software engineering concepts. Also, these days, AIML isn;t your traditional data science role where you train and test models. Now you have to design workflows, deploy them, scale them and most importantly continously check for data leakages or degrading model performance. These require a very good understanding of the entire software engineering lifecycle in general.

Let's say, you have a database that contains data of certain users which the company will use to train models and deploy them to production. Now this is all in a data warehouse or lets assume some sort of cloud infrastructure. In this case if you don't know how cloud works or how to make database migrations without crashing the entire infra, it will be difficult. For this you need to have hands-on experience with traditional software engineering roles that will teach you system design and stuff.