Generative AI lead for EMEA at AWS, working with European engineering teams to take AI projects out of prototype mode and into production. Drawing on years of advising companies at every stage of scale, he challenges teams to skip model-selection debates and focus purely on solving tangible operational bottlenecks.
At AWS, you'll find Eduardo in the technical weeds, balancing cloud infrastructure, API costs and data architecture to make machine learning run reliably at scale. His work spans large European enterprises and high-growth scale-ups trying to figure out how to make generative AI fast, secure and cost-effective.
He brings a developer-first perspective on what breaks when AI meets real-world users. Rather than showcasing flashy demos, he shares field notes on latency trade-offs, model context windows and the infrastructure choices required to run AI models without burning through engineering budgets.