Andrew Engel

WORK HISTORY

EDUCATION

SKILLS

ABOUT ANDREW ENGEL

I’m a founder, CTO, and AI/ML executive who has spent over two decades building production‑grade machine learning systems. I don’t just build models; I build the kind of systems that survive contact with messy data, skeptical customers, and real‑world business constraints.My career follows a simple pattern: I build things that work, I help leadership understand what’s actually possible, and I turn ambiguous problems into pragmatic, scalable code.What I’ve been up to lately:I’ve led AI teams at SAS, HP, DataRobot, Rasgo, weav.ai, and RapidCanvas, building everything from fraud detection engines to LLM agent systems. At RapidCanvas, I focused on normalizing financial documents and automating market research with LLMs. At weav.ai, I worked with banks, insurers, and pharma companies to move beyond the hype of generative AI and RAG into actual autonomous agents that solved real business problems.The Sports Analytics Chapter:A unique (and fun) part of my background is sports. As GM of Sports & Gaming at DataRobot, I worked with pro teams, leagues, and casinos to find high‑value AI wins in marketing, player identification, and on‑field performance. It’s where I learned that even the most advanced model is useless if it doesn’t solve a specific business (or game‑day) problem.Developer Roots:I’m still a builder at heart. I’ve written open‑source C libraries for time‑series feature engineering and PostgreSQL extensions (etu and pgetu) that are still running in production pipelines today. I like systems that are fast, simple, and built from first principles.The Founder / Fractional CTO Lens:I’ve helped early‑stage founders turn a slide deck into a product. Whether it’s building a FastAPI + Next.js stack for Uncap or architecting my current stealth startup, I focus on “Day 1” systems that don’t need to be ripped out on Day 100. I like helping founders make their first critical technical decisions — the ones that determine whether the next year is smooth sailing or a slow‑motion rewrite.The Reality Check:After 20+ years, I’ve learned that successful AI isn’t about complexity. It’s about:Framing the problem correctly (by far the hardest part).Simple, robust architectures over \"shiny\" tech.Thoughtful data engineering — where the real work happens.Tight feedback loops with the people actually using the tool.That’s the lens I bring to every project. If you’re building something ambitious and need a partner who has actually shipped at scale, I’d love to connect.