AI Engineering for ML Engineers
From design to monitoring: become autonomous across the entire modern AI agent stack: advanced RAG, agents with LangChain/LangGraph, the MCP protocol for tooling, and evaluation and observability with Langfuse.
What you'll learn
Advanced RAG
Understand LLMs and embeddings, and design a robust RAG architecture with hybrid search, metadata filtering, and other more advanced techniques.
Agents & MCP protocol
Master agent fundamentals (types, use cases), understand the MCP protocol and its interoperability challenges, orchestrate with LangChain/LangGraph, and connect an MCP server to your agents.
LLMOps
Evaluate, observe and steer GenAI applications in production with Langfuse, and architect them end to end.
The detailed programme
Seven progressive modules, from theoretical foundations (Transformers, embeddings) to a complete GenAI system with LangChain/LangGraph, Qdrant, MCP and Langfuse.
AI Engineering Fundamentals & RAG
3h30
Transformer architecture, embeddings, vector databases, and building a complete RAG pipeline with LangChain and LangGraph.
Objectives
- Master Transformer architecture, embeddings and vector databases
- Build a RAG pipeline: ingestion, chunking, hybrid search and metadata filtering
- Evaluate and optimize retrieval quality
What's covered
- Transformer architecture, embeddings and vector databases (Qdrant)
- RAG pipeline with LangChain and LangGraph: ingestion, chunking, hybrid search
- Metadata filtering and retrieval quality evaluation
- Pipeline optimization: chunking strategies and scoring
Your experts
Training investment
1 500,00 €
per participant
9 000,00 €
per session
80%
Satisfaction (2026) · 1 response
Fund your training through an OPCO
As a Qualiopi-certified training provider, the courses we offer can be funded through an OPCO (the French vocational-training funding body). Find which OPCO you depend on here.
Let's discuss your project
Personalised quotes, tailor-made formats, OPCO funding options: we answer all your questions.
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