AI Engineer - LLMs, RAG and agents

context

AI systems that are actually used

We work on AI platform and assistant projects built on language models, with industrialisation and production deployment in mind. The topics are concrete: document ingestion, agents, business-facing APIs.

The quality of an AI system comes from the data, the pipeline and the architecture, not from the prompt alone. That conviction shapes how we approach these projects.

the role

Building pipelines and agents that hold up in production

• Designing and building specialised AI assistants, with multi-step workflow and agent orchestration.
• Designing retrieval augmented generation pipelines: document ingestion, chunking, embeddings, ranking and retrieval.
• Continuously improving answer quality and running tooled model evaluation.
• Building APIs that expose assistants to business applications.
• Monitoring usage and keeping performance and inference costs under control.

technical environment

Technical environment

• Python
• LangChain
• LangGraph
• RAG
• FastAPI
• PostgreSQL / pgvector
• Docker
• Kubernetes
• Azure AI
• CI/CD

who we are looking for

Who we are looking for

• Experience in AI engineering, on language model and retrieval augmented generation work.
• Good command of Python and of an orchestration framework such as LangChain.
• Ability to build a complete pipeline and integrate it cleanly into an existing system.
• Understanding of answer quality, data structuring and performance concerns.
• An MLOps or LLMOps sensibility and experience with vector databases are appreciated.

This role is not a fit for a purely prompt-oriented or demo-oriented profile.

why this role

Why this role is worth your time

• The projects are industrialised and meant to be used, not presented.
• The technical content is substantial: architecture, pipeline, integration.
• The domain is still being built: your decisions shape the platform.
• You get the full technical context before any interview with the client.