Senior/Lead/Managers working in ML, is there a level trust when reviewing resume work experiences that the technical rigor is there but simply not being mentioned?

Does overtly mentioning every nitty gritty tool/library and code-level implementing come off as “they are being overly specific because they didn’t do much”, “they’re being overly specific because they’re unaware what’s considered simple versus difficult”

An example:

Case A: overtly descriptive

1. Utilized ensemble learning methods e.g random forest and boosting via scikit-learn to build regressions for weather-forecasting. Generated precision/recall metrics and quality visuals using matplotlib for post-training evaluation.

Skills: scikit-learn (SVM, random forest, boosting), Numpy, matplotlib, TF, etc etc

2. Trained a restricted Boltzmann machine via constrastive divergence on LLM-encoded user data for a movie recommendation system. Simulated user feedback indicated a 20% increase in viewer engagement.

Skills: Unsupervised learning, Keras, TF, recommendation systems, RBMs, etc etc

Case B: Higher-level/abstract

Developed deep-learning solutions to improve real-time performance on our perception platform for visual-odometry driven localization.

Skills: Tensorflow, TensorRT, Pytorch, scikit-learn, NumPy, OpenCV

In case B would you assume that said engineer used the tools mentioned, and thereby is skilled at them without them directly mentioning it in the description?

I’ll be job hunting in 2-3 years from now (currently been working for 6-7 years as an MLE), and I’m wondering if Case A gets to the point quicker for recruiters or does Case B provide simplicity yet being vague about implementation.