🤖 Member of Technical Staff — AI Product & Developer Experience, Amazon AGI
I make AI products successful by making them adoptable. I own the product adoption strategy for Amazon's next-generation AI, from market positioning through activation and retention, working across Research, Product, Engineering, and GTM. I define adoption funnels, run the data to find where users drop off, and drive the product changes that fix it.
I've spent nearly 20 years across data center infrastructure, data platforms, and AI, including six at AWS, first from Düsseldorf and then from the Bay Area. Along the way I co-wrote two O'Reilly books and built DeepLearning.AI courses that more than 485,000 people have taken.
I think like a PM, execute like a builder, and measure like a growth team. I build with AI every day to help shape where software engineering is heading and keep my product instincts sharp.
product-coach: a review layer for product decisions. Coaching skills object to your plan using your own numbers, and weeks later a scoreboard checks whether the objection was right. Advice that keeps score on itself.
bakeoff: pick a model by cost per correct call, not by leaderboard. Build an eval from your own agent logs, run it against any model on OpenRouter, and compare accuracy, latency, and cost side by side.
research: runnable notes on inference systems. Kernels, schedulers, profilers, and what it really costs to serve models on NVIDIA, AMD, TPU, Trainium, Cerebras, and d-Matrix silicon.
- O'Reilly co-author: Generative AI on AWS (2023) and Data Science on AWS (2021)
- DeepLearning.AI instructor and curriculum developer: Generative AI with Large Language Models (440,000+ learners) and Practical Data Science (45,000+ learners)
- Latest keynote: Perception Agents, AI Engineer World's Fair 2026, on agents that see and act on a screen the way people do
- Keynotes and conference chair: AI Engineer World's Fair, O'Reilly AI Superstream, Open Source Summit NA, MCP Dev Summit
- Co-host of the AI Performance Engineering Meetup and co-founder of the Düsseldorf chapter of Women in Big Data
In 2026 I switched sides of the classroom and completed eight Product School certifications. Each ended in a capstone: one product call, argued with data and shipped as a public repo. My favorite is the AI Evals capstone, because the honest answer turned out to be "don't ship it yet."
All eight capstones
- Product Leadership: turn a mental-wellness app into an AI companion that builds memory over time, without optimizing for dependency · repo · deck
- Product Analytics & Experimentation: trial conversion stuck at 2% for 13 months, so fix activation before acquisition · repo
- AI Product Management: an AI copilot that turns a P0 Slack thread into a ranked, source-cited shortlist · repo · prototype
- AI Product Strategy for Leaders: product-coach as a real strategy, with data flywheel, kill switch, pricing, and board pitch · repo · deck
- Claude Code for PMs: why four reliable responders went quiet after a release, from interviews, tickets, metrics, and routing code · repo · report
- Go-to-Market: group trips where every friend pays their own seat, launched city by city · repo · deck
- Agentic Workflows & Loops: an agent drafts the weekly leadership update, a critic checks it, and a human signs off · repo · pitch
- AI Evals: should this competitive-intelligence agent ship? Hold, and here's what would change the answer · repo · deck
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