AI Engineer
From embeddings and vector search to prompt engineering, fine-tuning, evals, and agentic systems.
1. Beginner
Foundations of modern AI, LLMs, and Python tooling.
Python for AI & Async Programming
Type hints, Pydantic data validation, async HTTP clients, numpy basics.
2. Foundation
Embeddings, vector databases, and similarity search.
Vector Embeddings & Semantic Search
Cosine similarity, dot product, HNSW indexing, chunking strategies.
3. Core Skills
Advanced Retrieval-Augmented Generation (RAG).
Production RAG Systems
Query re-writing, rerankers (Cohere), contextual compression, hallucination guards.
4. Framework
Agent architectures and tool calling.
Autonomous Agents & Function Calling
ReAct loop, function calling schemas, structured outputs, multi-agent orchestrations.
5. Real Projects
Ship an interactive enterprise agent.
Autonomous Code Review & Refactoring Agent
Git diff analysis, AST parsing, automated lint fixes, PR generation.
6. Advanced
Evaluations, observability, and fine-tuning.
LLM Evals & Tracing (LangSmith / Phoenix)
Ground truth datasets, LLM-as-a-judge, token latency budgets, LoRA / QLoRA fine-tuning.
7. Interview Prep
AI system architecture, scaling embeddings, and cost reduction.
AI System Design
Designing enterprise document search over 50M PDF pages with real-time permissions.