GenAI Engineer Career Path

Master Python fundamentals specifically for AI/ML: NumPy, Pandas, data manipulation, and ML libraries. Hands-on examples with real datasets.

💡 Master Python before diving into AI. Focus on NumPy and data manipulation.

Understand the fundamentals: supervised vs unsupervised learning, classification, regression, and model evaluation. No complex math, just clear explanations.

💡 Understanding ML basics helps you grasp how LLMs learn and work.

Deep dive into the architecture that powers ChatGPT, Claude, and all modern LLMs. Learn self-attention, positional encoding, and transformer architecture.

💡 Core architecture behind all modern LLMs. Essential deep dive.

Master Parameter-Efficient Fine-Tuning: LoRA, QLoRA, Prefix Tuning, and Adapters. Learn to customize LLMs for your use case without massive compute.

💡 Learn to customize LLMs efficiently. Critical for real-world applications.

Complete guide to Retrieval-Augmented Generation: vector databases, embedding models, chunking strategies, and production deployment. Build ChatGPT for your own data.

💡 Build ChatGPT for your own documents. Most common production use case.