AI Engineering Intern (AI Finance Apps) | €2,400 /mo. | Hamburg (hybrid)
Punti chiave dell'annuncio
Descrizione
What we're building
In 2026, companies across Europe have €1.4Trillion tied up in excess working capital. EY estimates that up to 5% of earnings are lost due to revenue leakage.
Transformance is building the AI-native Revenue Recovery Platform for Enterprises. We're replacing decades-old workflows in Order-to-Cash (O2C) with intelligent, agentic automation that actually works all-the while dealing with the messiness of fragmented enterprise data and workflows.
We're at the 0-to-1 stage. The platform is live, enterprise customers are paying, and we're profitable. Now we need engineers who want to build the foundation of what comes next.
Tasks
You’ll work directly with the founders and contribute to real production features, not toy projects. Typical projects include:
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AI workflows: implement and evaluate agent flows (classification, extraction, matching, next-best-action suggestions)
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Data plumbing: ingest, normalize, and validate finance data (CSV exports, APIs, documents)
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Product features: build UI and backend endpoints for finance apps (review queues, evidence packs, audit trails)
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Integrations: connect to ERPs and adjacent systems via APIs and webhooks
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Quality and reliability: write tests, add observability, reduce edge-case failures
Requirements
Must-have
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Strong fundamentals in JavaScript or TypeScript
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You can build small systems end-to-end (API + data + UI) with guidance
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You’re comfortable with ambiguity and can turn “messy problem” into a clear next step
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You write clearly (docs, PR descriptions, bugs)
Nice-to-have (we can teach you)
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React, Next.js
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Basic SQL and data handling
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Any exposure to LLM tooling (OpenAI, Anthropic, evals, agents)
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Curiosity about finance processes (AR, deductions, cash application, disputes)
Practical details
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Location: Hamburg, hybrid (remote days are fine; some in-person collaboration matters)
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Start: ASAP
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Duration: 6 months (option to extend and convert to full-time)
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Time: 40h/week
Dati principali
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