Flipside Crypto

Role Description 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. 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. Who We're Looking For - 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 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. - 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.

Who This Is For Most enterprise data is a mess. Not because companies lack data - they have too much of it, poorly connected, locked in silos, and inaccessible to the people who need it most. edisyl fixes that with agentic AI. But technology alone doesn't close deals. You've been in rooms where the problem is real, the technology is credible, and the deal still falls apart because no one could hold it together. You've been the one who held it together. You're energized by complexity, not paralyzed by it. You can sit in a technical discovery session, understand what's actually being built, and turn it into a commercial narrative that makes a VP of Data Engineering and a CFO both lean in. You build relationships that outlast any single deal. You write things people forward to their colleagues. You're not looking for a playbook. You're looking for the chance to write one. 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 We're looking for someone to work directly with the CEO to build enterprise relationships and close deals. This is not a quota-carrying AE role. It's closer to a founding GTM partner - someone with high EQ, creative deal instincts, and the ability to navigate complex organizations and build trust at multiple levels. What You'll Actually Do Work the enterprise deal cycle alongside the CEO - Own the mechanics: discovery prep, proposal drafting, SOW structuring, contract navigation, procurement sequencing - Help shape positioning for each account - this is consultative selling, not product selling - Keep deals moving; know where each opportunity stands and what it needs next Translate between our engineering team and the business - You don't need to write code, but you need to hold your own in a conversation about data pipelines, AI agents, and cloud infrastructure - Sit in technical discovery sessions; understand what our solutions actually do, then help package it into commercial proposals and SOWs that make sense - Work with our CTO and data science leads to scope what's deliverable, at what margin, on what timeline Build the playbook as we go - Develop repeatable engagement models - from POC to expansion SOW to subscription - that we can run across multiple accounts simultaneously - Think in deal economics: services vs. software mix, margin targets, expansion triggers, long-term account value - Build templates and frameworks so that enterprise sales becomes a system, not a series of heroic one-offs Own your relationships - The CEO opens doors; you build the ongoing relationships with champions, technical evaluators, and procurement contacts - Represent edisyl at industry events and in direct outreach - cold outreach to senior technical leaders at large enterprises should feel natural to you - Manage the rhythm of communication so the CEO's time is preserved for highest-leverage moments What Success Looks Like in Year One By the end of year one, you've closed two to three enterprise deals, each with a named champion you cultivated - not inherited. You've documented a playbook someone else could follow, and you've built a pipeline that reflects real relationships, not just names in a CRM. The measure isn't just revenue - it's whether the enterprise motion is a real, repeatable thing that exists because you built it. You'll know you're winning when the deals you're running have genuine momentum behind them, the champions in your accounts are calling you, and the CEO stops having to carry the deal mechanics and shows up only at the moments that need them. Compensation Competitive base salary, meaningful early-stage equity, and a variable component tied directly to the deals you close. Equity is real and early - this is a founding role and we price it that way. Variable is tied to deal outcomes you directly influence. We'll be transparent about the full picture in our first conversation. Who We're Looking For Experience - 4–8 years in a role that combined enterprise selling with technical understanding: enterprise sales at a data or AI company, solutions engineering, technical account management, or consulting with a hard pivot into tech - You've been close to six-figure-plus deals; you know what MSAs, SOWs, security reviews, and multi-stakeholder approval chains actually look like - You understand data and AI well enough to talk intelligently about pipelines, models, agents, and infrastructure - not to build them, but to sell them credibly and shape how they're deployed The Stuff That's Harder to Teach - High EQ. You read rooms, adjust your register, and build trust with skeptics. You know the difference between a champion and a blocker and how to work with both. - Creative deal instinct. When you hear a problem, you think about how to shape an engagement around it - not just match it to a pricelist. - Written clarity. Proposals, SOWs, deal memos. You write things people actually read. - Organized without being reminded. You build systems for tracking and follow-up. Deals don't fall through cracks. - Low ego, high agency. This role is whatever the deal needs it to be on any given day. You're comfortable with that. - AI-native. You stay close to how these tools are evolving and you use them. Bonus (Genuinely Not Required) - Background at Palantir, Databricks, Snowflake, or another company running a forward-deployed or consultative sales model - Management consulting with a move into enterprise tech - Experience working directly with a CEO or founder in a small-company environment - Familiarity with blockchain data, DeFi, or institutional crypto Why This, Why Now edisyl is at the moment where the technology is proven and the enterprise market is ready. We have active deployments, a real architecture, and inbound from firms that need what we've built. The person who takes this role will help build the enterprise motion from the ground up, working directly with a CEO who has done this for 30 years. That's a rare place to learn and a real chance to have outsized impact. To Apply Send an email to talent@flipsidecrypto.com with two things: 1) why this role fits where you are in your career right now, and why you are the best for the role; and 2) one example of a complex deal or engagement you helped drive to close. No cover letter template. Just tell us the story.