What’s the difference between working on Kaggle-style projects and real-world Data Science/ML roles

I’m trying to understand what Data Scientists or Machine Learning Engineers actually do on a day-to-day basis. What kind of tasks are typically involved, and how is that different from the kinds of projects we do on Kaggle?

I know that in Kaggle competitions, you usually get a dataset (often in CSV format), with some kind of target variable that you’re supposed to predict, like image classification, text classification, regression problems, etc. I also know that sometimes the data isn’t clean and needs preprocessing.

So my main question is: What’s the difference between doing a Kaggle-style project and working on real-world tasks at a company? What does the workflow or process look like in an actual job?

Also, what kind of tech stack do people typically work with in real ML/Data Science jobs?

Do you need to know about deployment and backend systems, or is it mostly focused on modeling and analysis? If yes, what tools or technologies are commonly used for deployment?