AI/ML Product Dream Team: Part II

AI/ML engineer

At this point, we’ve got a firm handle on our strategy, our data architecture, the capabilities of our customer/product data, and the models we’ll be using in our product. Our goals and objectives have been outlined and confirmed by the technical stakeholders on our leadership team. Taking a cumulative approach, by this point, we can outsource the execution of this planning-heavy part of establishing our AI/ML product or program. ML engineers are able to use the models and algorithms whipped up by our data scientists by incorporating—i.e., coding—them into the workflows or code repository for our product.

Again, because this is a role that will support the AI native product, this role will have the burden of getting in the weeds with our data and algorithms to see what comes out. This person can expect to do a lot of trial and error as they feel their way toward acceptable performance. Our ML engineer will use the data that’s been vetted by our data analysts and marry it with the algorithms our data scientists have selected in their work. Nothing really matters until we actually deploy something into production. The act of putting it all together and ultimately deploying the code that supports all the prior functions falls on the applied ML engineer who makes the ...