I’m supporting a fast-growing tech company that is building a next-generation search and intelligence layer for their platform. We’re looking for an AI Engineer with deep experience in search technologies, vector databases, and Retrieval-Augmented Generation (RAG).
This role sits in a high-impact product team working on a new initiative, with the freedom to shape architecture, improve search relevance, and build intelligent workflows used by thousands of enterprise users.
What you’ll work on
- Designing and implementing advanced search pipelines using vector databases and embeddings
- Building and optimising RAG-based systems for retrieval quality, latency, and scalability
- Developing internal tools and services that improve knowledge retrieval and search discovery
- Collaborating with product, data, and engineering teams to ship features end-to-end
- Evaluating new LLMs, embedding models, and retrieval techniques for production use
Must Have
- Strong experience with vector search (e.G., Pinecone, Weaviate, Milvus, Elasticsearch, OpenSearch)
- Hands-on experience building RAG systems in production
- Solid software engineering background (Python preferred)
- Familiarity with LLMs, embeddings, prompt engineering, and optimisation techniques
- Experience designing scalable search systems or AI-driven knowledge retrieval pipelines
- Based in (or willing to relocate to) Madrid;
hybrid working environment (3 Days onsite)
Nice to have
- Experience with ranking models, semantic search, and relevance tuning
- Knowledge of cloud platforms (AWS, Azure, or GCP)
- Previous work on internal developer tools or productivity/observability products