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Senior Applied AI Engineer, AI Platform (f/m/d)

bunch•Berlin
Full-time3-7
👁️ 0 views•📝 0 applications•Posted 9/17/2026•Expires 10/17/2026
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Job Description

bunch is building the backbone of private markets. We are enabling next-gen fund operations with one integrated system that combines secure data infrastructure, AI-powered workflows and expert fund services.

If you value ownership, growth through real responsibility, and working with a thoughtful, ambitious team, this role might be for you. Your Role As a Senior Applied AI Engineer on our AI Platform team, you take AI at bunch from working to relied upon.

We already run AI in production — a document extraction pipeline live in fund operations, agent workflows on Mastra, evaluations, and multi-provider fallback inside EU data residency.

Fund operations run on documents, deadlines and numbers that have to be right — subscription documents to parse, capital calls to chase, portfolio data to reconcile.

You build on that foundation: more agents taking that work off people's hands, the evaluations that prove they can be trusted with it, and the platform that lets every other team at bunch ship the same way.

This is end-to-end product engineering, not research: you own architecture, evaluation, integration, and deployment. Top Priorities Build and ship agents. Design agents that automate real fund-operations workflows, and own them from prototype through production and after.

They integrate with our services, data model, and authorization system — they don't sit beside the product as standalone prototypes. Evaluate and improve agent performance.

Build the evaluation layer: test cases built from real documents with the output we expect, regression suites in CI, human review where correctness is non-negotiable, and clear success criteria for an agent completing a complex task end to end. Then move the numbers that matter — accuracy, latency, cost.

Own the AI application architecture. Orchestration and multi-agent design, tool contracts, memory, and context engineering (RAG, MCP) with clear domain boundaries — plus the guardrails, approvals, and human-in-the-loop controls tha

Required Skills

AIdocument extractionagent workflowsevaluationsmulti-provider fallbackarchitectureintegrationdeploymentautomationdata modelauthorization systemevaluation layertest casesregression suitesaccuracylatencycostorchestrationmulti-agent designmemoryhuman reviewsuccess criteriaguardrailsapprovalshuman-in-the-loop controls

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