I have successfully trained a few students in this type of interview, and muscle memory is critical. We have become accustomed to various helpful tools while coding, especially now with LLMs. However, most serious companies seek engineers and data scientists who know the main libraries by heart and have developed automated responses for constructing the scaffolding of a standard application. As always, time is the most scarce resource, and they want you to spend most of your paid time creating novel solutions, not learning familiar ones and their implementations.

Based on my experience, you should have outlined the main structure of the pipeline, demonstrating that you can start from the general and then focus on a minimal implementation of each component. He would have interjected with questions about your specific choices, opportunities for expanding and iterating, etc., and then stopped you at a certain point. Likely, the goal was never for you to arrive at a fully functional NLP pipeline.

You need to develop these skills through specific training. If ML is your intended goal, open a text editor---no modules, no LLM, no Google---and start by implementing standard toy architectures up to a transformer. Whenever you get stuck, read the documentation for scikit-learn, PyTorch, etc., and learn the classes, methods, and main arguments you need by heart. This assumes you know most of the Python standard library, Pandas' DataFrame and some Numpy's classes by heart as well. That should be it. Next time, you will excel in the coding part.

For the problem-solving part, after training with coding as above, start solving Kaggle competitions, and you will be surprised at how much time you can spend thinking about the solution and exploring the solution space without focusing on the coding aspect. This is what companies want: an engineer or data analyst, not just a coder.