AI Engineer

Who Is This For

• Software engineers transitioning to AI Engineering roles
• ML engineers who want to cover the applied AI dimension
• Developers building LLM applications who need interview preparation
• Career changers from data science or traditional ML into GenAI

What You Will Learn

1. 1. RAG system design: retrieval, chunking, hybrid search, and evaluation
2. 2. Agent architectures: tool use, memory, planning, and failure recovery
3. 3. Fine-tuning vs RAG decision frameworks
4. 4. Evaluation metrics for LLM applications (MRR@10, NDCG, human eval)
5. 5. LLM system design interview walkthroughs
6. 6. Portfolio strategy for AI Engineer candidates

Sample Topics Covered

Design an enterprise knowledge assistant using RAG

When is fine-tuning worth it vs prompt engineering?

How to evaluate retrieval quality beyond accuracy

Agent failure recovery interview walkthrough

Frequently Asked Questions