Grounded, production-ready AI — RAG, LLMs and the data behind them.
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The hard part of AI in production is not calling a model — it is feeding it the right context and trusting the result. A language model is only as good as the data you ground it in, and the retrieval, pipelines and infrastructure around it are where real engineering lives.
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1 articles
Real-Time RAG in Python: Feed Your LLM Live Google Results (2026)
What if your RAG pipeline could pull fresh web context right before generating an answer? A step-by-step guide to building a live search retrieval layer with Bright Data's SERP API and Python.
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FAQ
What is your AI engineering background?
I build production AI systems — RAG pipelines, real-time data plumbing and MLOps — combining hands-on AI work (reflected in my CV as an AI engineer) with a decade of platform engineering at scale.
Are you available to hire?
Yes — RAG pipelines, AI infrastructure and MLOps, as a contract, consulting or selected full-time engagement, remote across the EU or on-site in Germany. I work fluently in English.
How do we start working together?
Tell me what you are taking from demo to production on the contact page, and I will reply with how I can help.
Taking AI from demo to production?
I build grounded RAG pipelines and reliable AI infrastructure — the data and MLOps layer that turns an impressive demo into a dependable system.
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