Forward-Deployed AI Data Engineer

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 years, 20+ chains, 700M+ resolved wallets — and now deploy that capability to enterprises navigating the same challenge: how to make their data work for them at scale, without armies of analysts. We have active deployments with a financial institution and Interlochen, a proven architecture, and inbound from firms that need what we've built. The technology works. What we're building now is the enterprise motion around it. The Role You embed inside client environments and make our AI agents work against data that was never prepared for them. You're not building generic tooling. You're solving a specific problem for a specific organization, with whatever data they actually have — CRMs, warehouses, email archives, document repositories. Every engagement ends with something measurable: leads written to CRM, pipelines running in production, briefings delivered to decision-makers. You work closely with the CTO and the Enterprise Data Strategist on each account. You are the person who makes the promise real. What You'll Actually Do - Lead technical onboarding and implementation from data environment discovery through production deployment - Build, configure, and troubleshoot data connectors, pipelines, and AI agent workflows inside client environments - Work directly with Forge, Lattice, and Stratum — our agent framework, orchestration layer, and semantic intelligence system - Serve as the primary technical point of contact for your accounts post-deployment - Surface what you're learning in the field — product gaps, failure modes, recurring patterns — back to engineering - Develop implementation playbooks from each engagement so the next one goes faster - Partner with the Enterprise Data Strategist and CEO on pre-sale scoping, technical discovery, and proof-of-concept builds What Success Looks Like in Year One You've run multiple enterprise implementations end-to-end and have something running in production at each one. You've built playbooks from what you learned, not just completed the engagements. Clients are asking for you by name. The team trusts you to go in alone and come back with something that works. The measure isn't how clean the code was. It's whether the agents produced the right outputs, reliably, in an environment that was never designed for them. Compensation Competitive base salary and meaningful early-stage equity. This is a foundational technical role and we price it that way. We'll be transparent about the full picture in our first conversation. Who We're Looking For Experience - 4–8 years combining hands-on data engineering with direct deployment or customer exposure — forward-deployed engineering, solutions engineering, data consulting, or technical implementation at a data or AI company - You've worked inside enterprise data environments and know what CRMs, warehouses, and legacy pipelines actually look like from the inside - SQL fluency — you think in queries, use DuckDB, dbt, or similar without looking things up; proficiency in Python preferred; comfortable reading and writing…