Summary
Sean Flowers is a Machine Learning Engineer with 11 years of experience applying advanced ML, C++, and Python to real-world problems across TV advertising, media optimization, insurance, and high-energy physics. He holds a PhD in Physics from Ohio State and brings production-grade ML engineering skills—building RAG-based chatbots, BERT models on SageMaker, OCR/CRAFT ACORD parsers, and end-to-end agentic AI for web/SMS/voice with Azure and Twilio integrations. Sean has shipped large-scale data pipelines and automated experiment infrastructure, translated petabyte-scale collider analysis into efficient distributed code, and routinely turns messy documents and time series into actionable product features. Colleagues rely on his combination of rigorous research instincts and pragmatic engineering: he designs both novel model architectures and the Terraform/Docker infrastructure to run them in production. Notably, he built ACORD parsing and dynamic ID&V conversational workflows that bridge document vision, NLP, and telephony—showing a rare full-stack ML-to-voice deployment capability.
11 years of coding experience
13 years of employment as a software developer
Lund University, SE, Lund University, SE at Machine Learning in High Energy Physics Summer School
Fermi National Acceleration Lab, US, Fermi National Acceleration Lab, US at CMS Data Analysis School
Doctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at The Ohio State University
Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at University of Florida