Applied AI Engineer

About CryptioCryptio is the leading enterprise-grade financial data transformation and digital asset ERP platform for accounting, financial reporting, tokenization compliance, and loan management solutions. It is a complete back and middle-office platform that delivers financial integrity and accountability for businesses in crypto. Its platform allows users to transform on-chain and off-chain crypto activity into auditable records for accounting, treasury, tax filings, license applications, loans activity, and regulatory reporting.Having recently announced our $45m series B, now couldn't be a better time to join!About the roleWe're hiring our first Applied AI Engineer to work directly with our Advisory Intelligence Product Engineer and the VP of Advisory Intelligence on net-new product. You'll have full-stack ownership from Postgres schema to React UI, and you'll collaborate directly with the VP on prompt architecture, workflow design, and product roadmap. The team is small on purpose.This is a high-autonomy seat with real room to grow. You'll experiment, ship, and shape the toolkit's direction. As the practice scales, the right person grows into a technical leader for the function.What you'll doThe Advisory Intelligence toolkit covers the full accounting workflow: transaction-level close processes, chart of accounts benchmarking, policy generation, fit-gap analysis against institutional standards, and financial reporting outputs. Each tool combines deterministic logic (the parts where being wrong has financial consequences) with AI layers that draw on a structured library of real client engagements. You'll work across the suite with primary ownership on net-new builds. Collaborate directly with the VP on prompt architecture, workflow design, and product roadmap. You're a builder who shapes what gets built. Own full-stack feature delivery from Postgres schema through API layer through React UI Build and maintain multi-step AI pipelines: prompt chaining, structured output parsing, confidence scoring, failure recovery, and audit-trail logging Integrate third-party financial data APIs into production tool workflows Build financial document ingestion: parse uploaded spreadsheets and structured reports into typed data models for downstream AI processing Maintain and extend the deterministic pre-processing and heuristic layers that make AI output reliable enough for audit Contribute to the AI output evaluation framework: structured test inputs, expected outputs, scoring rubrics Work cross-functionally with finance practitioners, product, design, and leadership to scope what gets built and push back on specs that miss What you'll bringCore engineering Production full-stack TypeScript: shipped real systems, not side projects Strong Node.js backend: REST APIs, middleware auth, async pipelines with batching, concurrency limits, retries, timeouts React and TypeScript for complex UI: state management with Zustand, schema validation with Zod, views that stay responsive at scale Exposure to distributed systems and microservice architectures: you understand the failure modes (partial writes, retries, ordering, observability) even if you haven't owned a microservice deployment end-to-end Data and Postgres Strong Postgres: data modeling for workflow and run-oriented schemas, indexing strategy, query troubleshooting at table scale Experience designing versioned config and audit-trail schemas. Append-only matters in compliance contexts and you've built for it. How you operate Strong communicator, written and verbal. You can explain a technical decision to a finance practitioner and a product spec back to engineering. You take initiative. When the spec is wrong, you propose alternatives. When something's broken, you don't wait to be asked. You work cross-functionally without friction. Finance, product, design, leadership are all on your week, not just other engineers. Comfortable owning a feature from schema to UI without handoffs Bonus point...