The data engineer’s role is shifting in the AI era. With LLMs and agents as new consumers, the challenge moves from SQL and schemas to semantics, context engineering, and making databases LLM-friendly. This session explores how data engineers can design semantic layers, document relationships, and expose data through MCPs and AI interfaces. We’ll highlight new skills required, illustrate pipelines that combine offline and online LLM processing, and show how data can serve business users, developers, and AI agents alike.
talk-data.com
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Speaker
Gal Peretz
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talks
Head of Artificial Intelligence
Carbyne
Gal Peretz is the Head of Artificial Intelligence at Carbyne, where he leads the development of advanced AI solutions for public safety. Previously, he served as Head of AI & Data at Torq and held engineering and research leadership roles at Microsoft, IBM, and several startups. Gal holds both a Bachelor’s and Master’s degree in Computer Science from the Technion, specializing in Natural Language Processing, and has authored multiple papers accepted at top conferences. He is the founder of Apriori.ai, a GenAI and NLP consulting firm, and co-hosts LangTalks, a leading podcast on AI engineering. Gal is passionate about building agentic applications for real-world impact and leads a global community of over 6,000 AI developers.
Bio from: Big Data LDN 2025
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