Tech Lead AI Engineering (m/w/d)

Job description

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 do Technical 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 au…