AI Engineering is basically Data Engineering focused on AI. When in "traditional" Data Engineering you create pipelines that store processed data in something like a Data Lake, in AI Eng. your end storage might be a specialized Feature Storage (like Feast or GCP Vertex AI).
There are some AI Engineers with strong scientific/mathematical background, but that's rare. Usually, you're paired with these ML people that actually develop and evaluate the models.
So my advice is to start with Data Engineering and then find a specialization AI. You should have a VERY solid foundation on scripting and programming, specially Python. Also, a lot of concepts of "data wrangling". Understanding how data flows from point A to point B, how the intermediate storages and streaming engines work, etc. Functional programming is key here.
> AI Engineering is basically Data Engineering focused on AI.
I work in machine learning and this isn't how I see it at all. Data engineering specifically evolved as a term to differentiate the people who work with data but don't work on ML/AI.
I work in machine learning and also in data engineering, and for most of my career the data engineering title was for people doing everything in the lifecycle outside of R&D workflows (building the models/model architecture itself). It's only very recently differentiated to MLE/DE, and even that is far from being a standard.
The skillset is largely the same, but with some specialized knowledge for ML data work.
This is exactly what I did years ago, and it's much closer to software engineering than building models (which a lot of commenters are conflating with MLE - but tbf the titles aren't delineated well in practice).
There are some AI Engineers with strong scientific/mathematical background, but that's rare. Usually, you're paired with these ML people that actually develop and evaluate the models.
So my advice is to start with Data Engineering and then find a specialization AI. You should have a VERY solid foundation on scripting and programming, specially Python. Also, a lot of concepts of "data wrangling". Understanding how data flows from point A to point B, how the intermediate storages and streaming engines work, etc. Functional programming is key here.
[0] https://github.com/feast-dev/feast