Tech Lead AI Engineering (m/w/d)

For a young company in the rapidly growing AI implementation market, we are looking for a Tech Lead, starting as soon as possible, ideally in early November. The company runs LLM-based systems in production: content generation pipelines, retrieval-augmented generation (RAG) on internal documents, and automated workflows closely integrated with their business systems. The entire stack is built on TypeScript and Next.js. These systems are already live. As Tech Lead, you take ownership of them, increase their reliability and quality, and expand them to new use cases, while being technically responsible for a team. The role brings together applied LLM engineering and solid backend engineering in TypeScript. Training or fine-tuning foundation models is not part of the job. Stack: TypeScript, Next.js, Node.js, Postgres with pgvector, Docker, Azure, Anthropic and OpenAI APIs, Vercel AI SDK. What you’ll doTechnical responsibility for the team, including architecture decisions and code reviews Technical guidance for the team and point of contact for non-technical colleagues Take over and maintain the existing LLM pipelines: review the current architecture, identify failure modes, prioritise fixes, and refactor and extend the systems without disrupting production Own the RAG systems from 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 all retrieval systems Develop content generation pipelines that deliver consistent quality at scale, including human review steps Build and run 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 Set up an evaluation framework for systems that currently run without one: golden datasets based on real production failures, retrieval metrics and more Implement observability along the entire request path Optimise cost and latency through prompt caching, batching, model routing and smaller models where it makes sense Identify where deterministic logic is the better choice and implement it accordingly Work directly with non-technical colleagues to define and validate automated processes What you bringProfessional experience with at least one LLM-based system in production, including responsibility for its operation and incident handling Advanced TypeScript and Node.js skills: strict typing of non-deterministic model output, async and concurrency patterns, streaming responses, structured error handling Production experience with Next.js: App Router, route handlers, server actions, streaming to the client Proven track record of taking over and improving existing codebases under production traffic Hands-on retrieval expertise: hybrid search, choosing embedding models, cross-encoder reranking, metadata filtering, permission-aware retrieval and structured diagnosis of poor retrieval quality Experience working with real-world documents: PDFs with tables, scanned material, DOCX, HTML, including layout-aware parsing, OCR and evidence-based chunking Structured outputs and tool calling are part of your daily work: JSON Schema, Zod or similar runtime validation, function calling, handling malformed or partial output, context window management Experience designing and running LLM evaluations Experience with LLM tracing and evaluation tools in a TypeScript codebase (e.g. Braintrust, Langfuse, Promptfoo, OpenTelemetry or Arize Phoenix) Familiarity with Postgres including vector search (pgvector or a comparable vector store), Docker, Git, CI/CD and at least one major cloud platform Practical experience with the Anthropic and/or OpenAI TypeScript SDKs Confident communication in English, German is a plus Nice to haveDurable workfl