Level 1: Beginner
Core concepts, models & prompting
Level 2: Intermediate
Data layer, embeddings & RAG
Level 3: Advanced
Agents, MCP & Multimodal
AI Engineer Roadmap
Based on roadmap.sh/ai-engineer
Builds applications on top of pre-trained models & APIs
(distinct from ML Engineers who train models from scratch)
Function Calling & Structured Output
- Function Calling concepts
- Tool use patterns
- Structured output formats (JSON, schemas)
AI Agents
- What are AI Agents?
- Agent Use Cases
- ReAct Prompting
Frameworks:
- OpenAI AgentKit & Agent SDK
- Claude Agent SDK
- Manual Implementation
Model Context Protocol (MCP)
- MCP Host / Server / Client
- Data Layer & Transport Layer
- Building an MCP Server
- Building an MCP Client
- Connect to Local Server
Multimodal AI
- Image Understanding
- Image Generation (DALL-E)
- Video Understanding
- Audio Processing
- Text-to-Speech / Speech-to-Text
Tools: OpenAI Vision API, Whisper API, HuggingFace Models, LangChain/LlamaIndex multimodal
Core LLM Concepts
- Large Language Models (LLMs)
- Tokens & Context Window
- Inference vs Training
- AI vs AGI
- Prompt Engineering (overview)
- Vector DBs & RAG (overview)
Embeddings
- What are Embedding
- Semantic Search
- Recommendation Systems
- Anomaly Detection
- Data Classification
Providers: OpenAI, Cohere, Gemini, Jina, Sentence Transformers, HuggingFace
Pre-trained Models
- Anthropic Claude
- OpenAI GPT / o-series
- Google Gemini
- Meta Llama (open source)
- Mistral (open source)
- Cohere
Prompt Engineering
- Zero-Shot Prompting
- Few-Shot Prompting
- Chain of Thought (CoT)
- ReAct (Reasoning + Acting)
- Top-K / Top-P Sampling
- Robust prompt design
Closed Source APIs
- OpenAI Response API
- Google Gemini API
Vector Databases
- Indexing Embeddings
- Similarity Search
Options:
Chroma, Pinecone, Weaviate, FAISS, LanceDB, Qdrant, Supabase, MongoDB Atlas
RAG (Retrieval-Augmented Generation)
- What is RAG? / Use Cases
- RAG vs Fine-tuning
Pipeline:
Chunking → Embedding → Vector DB → Retrieval → Generation
Frameworks: LangChain, LlamaIndex, Haystack, RAGFlow
AI Safety & Ethics
- Prompt Injection Attacks
- Bias & Fairness
- Security & Privacy Concerns
- Adversarial Testing
- Content Moderation APIs
- Add end-user IDs in prompts
- Constrain inputs & outputs
- Know your customers / use cases
Introduction
- What is an AI Engineer?
- AI Engineer vs ML Engineer
- Roles & Responsibilities
- Impact on Product Development
Open Source Tools
- Ollama (run models locally)
- LM Studio
- Hugging Face Hub
- Hugging Face Inference SDK
- Transformers.js