[Hiring] Forward-Deployed AI Data Engineer @Edisyl

Role Description

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.

Qualifications

• 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
• Experience working inside enterprise data environments and familiarity with CRMs, warehouses, and legacy pipelines
• 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

Requirements

• 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
• 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
• Experience in financial services or insurance data environments

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.