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edisyl builds AI solutions that transform messy institutional data into actionable decisions, workflows, and outcomes. The company emerged from 8 years of blockchain data infrastructure work (700M+ resolved wallets across 20+ chains) and now applies that capability to enterprise clients facing the same core challenge: making their data work at scale without armies of analysts.
As a Forward-Deployed AI Data Engineer, you will embed inside client environments and make AI agents work against data that was never prepared for them. This is not generic tooling—you solve specific problems for specific organizations using whatever data they actually have: CRMs, warehouses, email archives, document repositories. Every engagement produces something measurable: leads written to CRM, pipelines running in production, briefings delivered to decision-makers.
You will lead technical onboarding and implementation from data environment discovery through production deployment. You build, configure, and troubleshoot data connectors, pipelines, and AI agent workflows inside client environments using edisyl's proprietary stack: Forge (agent framework), Lattice (orchestration layer), and Stratum (semantic intelligence system). You serve as the primary technical point of contact for accounts post-deployment and surface learnings—product gaps, failure modes, recurring patterns—back to engineering.
Success in year one means running multiple enterprise implementations end-to-end with production deployments at each, building playbooks from learnings, having clients request you by name, and earning team trust to operate independently. The measure of success is whether agents produce the right outputs reliably in environments never designed for them—not code elegance.
You combine 4–8 years of hands-on data engineering with direct deployment or customer exposure (forward-deployed engineering, solutions engineering, data consulting, or technical implementation at data/AI companies). You have deep SQL fluency, Python proficiency, and hands-on experience building or deploying AI agent workflows. You understand enterprise data environments from the inside: CRMs, warehouses, legacy pipelines. You have unstructured data instincts, bias toward output over elegance, client-facing comfort, and strong opinions on why AI deployments fail on data rather than models. Bonus experience includes forward-deployed or consultative technical models (Palantir, Scale AI), blockchain/DeFi/crypto infrastructure, or financial services/insurance data environments.
