Tech Lead AI Engineering (m/w/d)

Meyandy LLC; Köln; Vor Ort; EUR 90.000 - 150.000; Vollzeit

Zusammenfassung

Nejo in Cologne seeks a Tech Lead AI Engineering to own and improve live LLM-based systems, combining applied LLM engineering with strong backend TS. You will guide a team, ensure reliability, and extend capabilities to new use cases while maintaining production stability.

The role emphasizes architecture, code quality, and collaboration with non-technical stakeholders for automated processes and workflows integrated with internal business systems.

Qualifikationen

• Professional experience with at least one LLM-based system in production use, including operation and incident handling.
• Advanced TypeScript and Node.js: strict typing, async/concurrency, streaming responses, error handling.
• Next.js in production: App Router, route handlers, server actions, streaming to client.
• Proven ability to take over and improve existing production codebases.
• Practical retrieval expertise: hybrid search, embedding models, cross-encoder reranking, permission-aware retrieval.

Aufgaben

• Provide architecture decisions and code reviews for the team.
• Offer technical guidance and liaise with non-technical colleagues.
• Maintain and extend LLM pipelines without disrupting production.
• Own end-to-end RAG systems: ingestion, chunking, indexing, retrieval, and grounded generation with citations.
• Implement chunk-level access control, index freshness, tenant isolation across retrieval systems.
• Develop content generation pipelines delivering consistent quality at volume with human review steps.
• Build and operate automated workflows against ERP/CRM/internal APIs with durable execution and approval steps.
• Establish evaluation framework using production-derived datasets and metrics.
• Implement observability across the full request path, optimize cost and latency.

Kenntnisse

LLM in production

TypeScript

Node.js

Next.js

Retrieval augmentation

Docs processing

JSON Schema / validation

LLM evaluation tooling

Postgres vector search

Docker

Cloud platforms

SDKs (OpenAI/Anthropic)

English communication

Tools

pgvector

Docker

Git

CI/CD

OpenAI SDK

Anthropic SDK

Vercel AI SDK

Jobbeschreibung

For a young company in the fast-growing AI implementation market we are looking for a Tech Lead, starting end of October or early November. The company operates LLM-based systems in production: content generation pipelines, retrieval-augmented generation (RAG) over internal documents, and automated workflows deeply integrated with their business systems.

The stack is TypeScript and Next.js end to end. These systems are already live.

As Tech Lead you take ownership of them, improve their reliability and quality, and extend them to new use cases, while taking technical responsibility for a team.

The role combines applied LLM engineering with solid backend engineering in TypeScript. It does not involve training or fine-tuning foundation models.

Stack: TypeScript, Next.js, Node.js, Postgres with pgvector, Docker, Azure, Anthropic and OpenAI APIs, Vercel AI SDK.

Tasks

• Technical responsibility for a team: architecture decisions and code reviews
• Technical guidance and interface to non-technical colleagues
• Take over and maintain the existing LLM pipelines: assess the current architecture, identify failure modes, prioritise fixes, and refactor and extend without disrupting production
• Own the RAG systems end to end: document ingestion and parsing, chunking, indexing, hybrid retrieval (BM25 and vector), query rewriting, reranking, 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

Requirements

• 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 or 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 is a plus

Nice to have

• Nice to have: durable workflow execution for long-running, unattended processes (Temporal, Inngest, or comparable)
• Nice to have: agent orchestration in production, tool calling, recovery, multi-step workflows (Vercel AI SDK, LangGraph, Mastra, …)

Tech Lead AI Engineering (m/w/d) — Nejo, Cologne.