Tech Lead AI Engineering (m/w/d)

Nejo · Cologne · Hybrid

• Employment typeFull-time
• Experience levelSenior
• Salary signalSalary not listed
• Last checked1 hour ago
• Source valid throughNov 21, 2026

Role Overview

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 automated processes What you bring Professional 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 have Durable workflow execution for long-running, unattended processes (Temporal, Inngest or similar) Agent orchestration in production, tool calling, recovery, multi-step workflows (Vercel AI SDK, LangGraph, Mastra, Claude Agent SDK, MCP TypeScript SDK) Experience integrating enterprise systems such as ERP or CRM platforms like SAP Security and data protection in LLM systems, defences against prompt injection and data exfiltration, PII handling, GDPR-compliant design, EU-hosted or self-hosted inference Structured or graph-based retrieval for entity-heavy dataexperience Migrating live pipelines to a new model, embedding model or index without loss of quality Azure DevOps, Pipelines, Repos and Boards Benefits Hybrid setup with 2 office days per week in a bright office in Cologne’s Belgian Quarter (with two balconies and the best view of the city ;)) AI implementation, the growth market of the coming years Trust-based working hours with compensation for overtime Lots of creative freedom and space for your own ideas Continuous professional, strategic and technological development An open feedback culture, transparent communication and short decision-making paths Regular team events and workations Salary from €60,000 gross per year, with room for more depending on experience The company is an international team and is actively committed to an inclusive workplace. Applications from people of every gender and identity are welcome, regardless of origin, age, religion, sexual orientation or disability. As women remain underrepresented in tech, women are especially encouraged to apply. We look forward to receiving your application! Find more English Speaking Jobs in Germany on Arbeitnow

Why It Matches

This full-time role is listed as hybrid in Cologne. The source does not expose a salary range, so confirm compensation before investing in a long application. The match score reflects role clarity, work mode, location, freshness, salary visibility, and description depth—not a promise that the employer will select a candidate.

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