flipsidecrypto - Forward-Deployed AI Data Engineer
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Requirements
• 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. • 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
Benefits
• 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. • 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. • 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.
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