ML and AI/GenAI interview resources

April 2025 cohort. Booster GenAI interview content for previous cohorts.

I run a course on O’Reilly titled Machine Learning Interviews. In the last few, I have added content on GenAI / AI interviews along with the demand of the attendees.

As mentioned in the course: GenAI is powered by machine learning. For example, ChatGPT uses GPT models, such as GPT-4o, GPT-4.1, GPT-4 etc. GPT models, among other competitors, as Large Language Models (LLMs) and/or multimodal. These leverage traditional Natural Language Processing (NLP) techniques and machine learning (ML) to train. The same logic applies for image models used for GenAI, multimodal models, and so on.

If you have attended this cohort, or a previous one and want to gain access to this “booster” content, this post is for you.

Day 2 resources:

GenAI interview questions

Source: My slides from when I was a guest speaker at AI by Hand’s Llama4 lecture. See the recording: Professor Tom Yeh’s LinkedIn

Machine learning engineer courses - only pick and choose one from each category and ignore categories that are less relevant. In this course, I emphasize ruthless prioritization.

• https://www.coursera.org/collections/machine-learning-engineer-development-plan

GenAI related roles I shared live on screen. RAG was mentioned the most often as a GenAI skill on the sample size. Note: these roles are on the senior side because of LinkedIn’s recommendation system, but the core tools and technologies will tell you about the current trends just as well.

• AI Engineer, Synopsys - https://careers.synopsys.com/job/mississauga/ai-engineer-staff/44408/76799023264

• AI Engineer, Honeycomb - https://job-boards.greenhouse.io/honeycomb/jobs/4603796008

• Applied Scientist - Multimodal LLMs, Samsara - https://www.samsara.com/ca/company/careers/roles/6737314

• Applied Scientist, NLP/IR/GenAI - Thomson Reuters - https://careers.thomsonreuters.com/us/en/job/JREQ188745/Senior-Applied-Scientist-NLP-IR-GenAI

• GenAI ML/MLOps Engineering, S&P Global - https://careers.spglobal.com/jobs/312048

Day 1 resources:

Choosing an AI/ML/DS side project: https://www.susanshu.com/data-science-side-project

Model fine-tuning, focusing on Hugging Face and Google Colab resources for free:

• https://platform.openai.com/docs/guides/fine-tuning

• https://huggingface.co/blog/dpo-trl

• https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/2.Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.ipynb

Learn more about GenAI project details

• https://www.elastic.co/blog/elastic-security-generative-ai-features

• https://susanshu.substack.com/p/generative-ai-project-planning

MLE/AI career ladder examples:

• https://dropbox.github.io/dbx-career-framework/ic2_machine_learning_engineer.html

Conclusion

And that’s it for this cohort’s summary. Want to get more content like this? Support my work by leaving a review on the O’Reilly platform on my book/course, and share it with your friends!

https://www.oreilly.com/library/view/machine-learning-interviews/9781098146535/