I don’t just want to use an LLM. I want to understand how to build a reliable end-to-end RAG system.

In this video, I present the current state of my RAG project, running locally with Docker.

The MVP already includes:

— an API built with FastAPI; — vector indexing and search with Qdrant; — local generation with Ollama; — answers accompanied by their sources; — a refusal mechanism when the corpus does not contain enough information to answer.

My next steps focus on hybrid search, reranking, automated evaluation using recall@k, MRR and groundedness, as well as latency optimisation and cloud deployment.

After several years working in information systems, Data/BI and the operation of critical Big Data platforms, I am now strengthening my specialisation in AI Engineering through this project and regular mentoring with a Microsoft CoreAI engineer, with a focus on robustness, evaluation and production readiness. Microsoft’s CoreAI organisation is focused on building the end-to-end AI application and agent stack, so “Microsoft CoreAI engineer” is the appropriate English wording here.

I am looking for an AI Engineer opportunity focused on RAG and LLM systems, or a hybrid Data/Platform-to-AI role within a team working on real-world projects.

In particular, I can bring:

— hands-on experience with critical production environments; — a strong foundation in Data Engineering, Linux, Python, Docker and CI/CD; — an approach to AI that considers reliability, evaluation, latency and operability from the start.

To my network: if you know a team looking for this kind of profile, an introduction, a targeted recommendation or sharing this post could create a genuinely valuable connection for both sides.

I would also be happy to connect with recruiters, technical leaders and engineers working on these challenges. #AIEngineer #RAG #LLM #DataEngineering #OpenToWork