Question for MLEs: How often are you writing your models from scratch in TF/PyTorch?

I have about 8 years of experience mostly in the NLP space although i've done a little bit of vision modeling work. I was recently let go so I'm in the midst of interview prep hell. As i'm moving further along in the journey, i'm feeling i have some gaps modeling wise but I'm just trying to see how others are doing their work.

Most of my work the last year was around developing MCP servers/back end stuff for LLMs, context management, creating safety guardrails, prompt engineering, etc. My work before that was using some off the shelf models for image tasks, mostly using models I found on github via papers or pre-trained models on HuggingFace. And before that I spent most of my time around feature engineering/data prep and/or tuning hyperparamters on lighter weight models (think XGBoost for classification, or BERTopic for topic modeling).

I've certainly read books/seen code that involves hand-coding a transformer model from scratch but I've never actually needed to do something like this. Or when papers talk about early/late fusion layers or anything more complex than a few layers, I'd probably have to look up how to do it for a day or two before getting it going.

Am i the anomaly here? I feel like half my time has been doing DS work and the other half plain old engineering work, but people are expecting more NN coding knowledge than i have and frankly it feels bad, man. How often are y'all just looking for the latest and greatest model on UnSloth/HF instead of building it yourself?

Brought to you from the depths of unemployment depression....