AI Engineer (m/w/d)

Your profile

• Professional experience with at least one LLM-based system in production use, including responsibility for its operation and incident handling
• TypeScript and Node.js at an advanced level: strict typing of non-deterministic model output, async and concurrency patterns, streaming responses, structured error handling
• Next.js in production: App Router, route handlers, server actions, streaming to the client
• Demonstrably taken over and improved existing codebases under production traffic
• Practical retrieval expertise: hybrid search, embedding model selection, cross-encoder reranking, metadata filtering, permission-aware retrieval, and structured diagnosis of poor retrieval quality
• Experience processing real-world documents: PDFs with tables, scanned material, DOCX, HTML - including layout-aware parsing, OCR, and evidence-based chunking
• Structured outputs and tool calling as part of your everyday work: JSON Schema, Zod or comparable runtime validation, function calling, handling of malformed or partial output, context window management
• Designed and run LLM evaluations
• Experience with LLM tracing and evaluation tooling in a TypeScript codebase (e.g. Braintrust, Langfuse, Promptfoo, OpenTelemetry/Arize Phoenix)
• Familiar with Postgres including vector search (pgvector or a comparable vector store), Docker, Git, CI/CD, and one major cloud platform
• Working experience with the Anthropic and/or OpenAI TypeScript SDKs
• Confident communication in English; German mind. B2

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Nice to have

• Durable workflow execution for long-running, unattended processes (Temporal, Inngest, Trigger.dev or comparable)
• Agent orchestration in production: tool calling, recovery, multi-step workflows (Vercel AI SDK, LangGraph, Mastra, Claude Agent SDK, MCP TypeScript SDK)
• Integration experience with enterprise systems such as ERP or CRM platforms like SAP
• Security and data protection in LLM systems: prompt injection and data exfiltration defences, PII handling, GDPR-compliant design, EU-hosted or self-hosted inference
• Structured or graph-based retrieval for entity-heavy data
• Experience migrating live pipelines to a new model, embedding model or index without quality regression
• Azure DevOps: Pipelines, Repos and Boards

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Your mission

• Take over and maintain existing LLM pipelines: assess the current architecture, identify failure modes, prioritise fixes, and refactor and extend the systems without disrupting production
• Own our RAG systems end to end: document ingestion and parsing, chunking, indexing, hybrid retrieval (BM25 and vector), query rewriting, reranking, and grounded generation with citations
• Implement and maintain chunk-level access control, index freshness and tenant isolation across retrieval systems
• Develop content generation pipelines that deliver consistent quality at volume, including human review steps
• Build and operate automated workflows against internal and third-party business systems (ERP, CRM, email, internal APIs), with durable and idempotent execution, retry and dead-letter handling, and approval steps for irreversible actions
• Establish an evaluation framework for systems currently running without one: golden datasets derived from observed production failures, retrieval metrics, and more
• Implement observability across the full request path
• Optimise cost and latency through prompt caching, batching, model routing and use of smaller models where appropriate
• Assess where deterministic logic is the better solution and implement it accordingly
• Work directly with non-technical colleagues to specify and validate automated processes

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