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jeroenherczeg/README.md

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Senior software engineer building AI systems for production.

I have 20 years of engineering experience across software platforms, distributed systems, microservices, Kubernetes, and product teams. My current work focuses on retrieval-augmented generation, AI agent orchestration, and practical AI engineering.

Most recently, I contributed agents and orchestration work to the Google + BBC AI Agents demo at IBC2025, winner of the Broadcast Tech Innovation Award.

I wrote Retrieval-Augmented Generation: An Engineer's Guide to Building RAG Systems with Your Own Data now available on Leanpub.

I write about practical AI engineering at herczeg.be/blog.

Working on

  • Retrieval-augmented generation
  • AI agent orchestration
  • Production AI systems
  • Evaluation and observability for RAG
  • Distributed systems and Kubernetes

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  1. sentence-encoder-bge-m3 sentence-encoder-bge-m3 Public

    FastAPI implementation of BAAI/bge-m3 encoder, containerized for Kubernetes deployment

    Python 2 2

  2. eu-ai-act-dataset eu-ai-act-dataset Public

    Reproducible pipeline that turns the official EU AI Act Formex XML into a structured, multilingual, retrieval-ready Hugging Face dataset.

    Python 1

  3. google/virtual-broadcast-production-assistant google/virtual-broadcast-production-assistant Public

    Python 29 26

  4. go-git go-git Public

    Forked from src-d/go-git

    A highly extensible Git implementation in pure Go.

    Go 1