Job Description
Who This Is For
Most enterprise data environments were never built to be AI-ready. They were built to survive — cobbled together over years of acquisitions, migrations, and workarounds. The data exists. It's scattered, unlabeled, and structurally hostile to anything that assumes cleanliness.
You've worked in those environments. Not as an observer — as the person who had to make something work inside them. You know the difference between a schema that looks clean and one that is clean. You've hit the accuracy cliff with an LLM and built around it instead of pretending it wasn't there.
You're not looking for a greenfield project with perfect infrastructure. You're looking for the genuinely hard problem — and the chance to solve it in front of a customer who needs it solved.
About edisyl
edisyl builds AI solutions that turn messy institutional data into decisions, workflows, and outcomes. We came out of blockchain data infrastructure — 8 y...