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 Fidelity 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 API integrations

- Hands-on experience building or deploying AI agent workflows; you know where LLMs break against real data problems

The Stuff That's Harder to Teach

- Unstructured data instincts. No schema, no labels, no consistent format — and you didn't flinch.

- Bias toward output. You care more about whether the agent's results were right than whether the code was elegant. You'd rather prototype a fix than write a ticket about it.

- Client-facing comfort. You can sit in a room with a CTO and explain why their data isn't AI-ready without making them feel bad about it.

- Strong opinions. You have a clear view on why most AI deployments fail on data, not model — and you've built something that proved it.

Bonus (Genuinely Not Required)

- Experience at a company running a forward-deployed or consultative technical model — Palantir, Scale AI, or similar

- Familiarity with blockchain data, DeFi, or institutional crypto infrastructure

- Financial services or insurance data environments

Why This, Why Now

edisyl is at the moment where the technology is proven and the enterprise market is ready. The person who takes this role will be among the first technical people embedded with customers — shaping how the product evolves and what the deployment playbook becomes. That's a rare kind of leverage, and a real chance to build something that outlasts any single engagement.

To Apply

Complete the online application and include responses to: 1) why this role fits where you are in your career right now, and why you are the right person for it; and 2) one example of a messy data problem you had to solve in production — what the environment looked like, what broke, and how you fixed it.

No template. Just tell us the story.