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Hey everyone,

I’m trying to map out a clear path to become a Generative AI Engineer and I’d love some guidance from those who’ve been down this road.

My background: I have a solid foundation in data processing, classical machine learning, and deep learning. I’ve also worked a bit with computer vision and basic NLP models (RNNs, LSTM, embeddings, etc.).

Now I want to specialize in generative AI — specifically large language models, agents, RAG systems, and multimodal generation — but I’m not sure where exactly to start or how to structure the journey.

My main questions:

* What core areas in NLP should I master before diving into generative modeling?
* Which topics/libraries/projects would you recommend for someone aiming to build real-world generative AI applications (chatbots, LLM-powered tools, agents, etc.)?
* Any recommended courses, resources, or GitHub repos to follow?
* Should I focus more on model building (e.g., training transformers) or using existing models (e.g., fine-tuning, prompting, chaining)?
* What does a modern Generative AI Engineer actually need to know (theory + engineering-wise)?

My end goal is to build and deploy real generative AI systems — like retrieval-augmented generation pipelines, intelligent agents, or language interfaces that solve real business problems.

If anyone has a roadmap, playlist, curriculum, or just good advice on how to structure this journey — I’d really appreciate it!

Thanks 🙏