Applied AI Engineer

About Cryptio Cryptio 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 role We'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 do The 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 bring Core 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