🤖 AI Engineer Roadmap 2026
A complete learning path to become a production-ready AI Engineer in 2026.
📋 Prerequisites
- Solid Python (decorators, async, typing, OOP)
- Basic linear algebra & probability
- Comfortable with Git and CLI
- Some experience with REST APIs
🗺️ 12-Week Curriculum
- Weeks 1-2: Complete Karpathy's "Let's Build GPT from Scratch" + Neural Networks course
- Weeks 3-4: Build 3 projects using OpenAI API (chatbot, RAG system, agent)
- Weeks 5-6: Learn DSPy + LangGraph. Build a multi-agent research system
- Weeks 7-8: Deploy a model with vLLM, create a FastAPI wrapper, add monitoring
- Weeks 9-12: Fine-tune a small model using Unsloth/Axolotl, evaluate with lm-eval-harness
🛠️ Essential Tools to Master
Frameworks
LangChain, DSPy, LlamaIndex, Haystack
LangChain, DSPy, LlamaIndex, Haystack
Vector DBs
Pinecone, Qdrant, Chroma, Weaviate
Pinecone, Qdrant, Chroma, Weaviate
Serving
vLLM, Ollama, TGI, Triton
vLLM, Ollama, TGI, Triton
Training
Axolotl, Unsloth, TRL, HuggingFace
Axolotl, Unsloth, TRL, HuggingFace
Agents
LangGraph, CrewAI, AutoGen
LangGraph, CrewAI, AutoGen
Eval
lm-eval-harness, DeepEval, RAGAS
lm-eval-harness, DeepEval, RAGAS
🎯 Key Skills to Develop
🔤 Prompt Engineering
Chain-of-thought, few-shot, structured outputs, system prompt design, RAG patterns
🏗️ System Design
Caching strategies, rate limiting, async processing, batch inference, model routing
📊 Evaluation
A/B testing, LLM-as-judge, automated eval pipelines, human eval, regression testing
🔒 Safety & Monitoring
Guardrails, PII detection, content moderation, drift monitoring, cost tracking