6 of 6 Permanent Weaviate Jobs in the UK excluding London

Hugging Face, including agentic and multi‐step workflows* **Proven experience designing and implementing Retrieval**‐**Augmented Generation (RAG) systems**, including vector databases (pgvector, Pinecone, Weaviate, FAISS, etc.) and advanced retrieval strategies (hybrid search, re‐ranking, metadata filtering)* **Strong understanding of prompt engineering, workflow design, and Gen**‐**AI optimization**, balancing latency, cost ...

Hugging Face, including agentic and multi‐step workflows Proven experience designing and implementing Retrieval‐Augmented Generation (RAG) systems, including vector databases (pgvector, Pinecone, Weaviate, FAISS, etc.) and advanced retrieval strategies (hybrid search, re‐ranking, metadata filtering) Strong understanding of prompt engineering, workflow design, and Gen‐AI optimization, balancing latency, cost ...

LangChain, Semantic Kernel, or AutoGen. Hands-on experience with Model Context Protocol (MCP) implementations. Strong understanding of vector databases and technologies such as Pinecone, Weaviate, FAISS, or Azure AI Search. Experience building and supporting Retrieval-Augmented Generation (RAG) solutions. Experience working with OpenAI and other modern AI platforms. Expertise ...

LangChain, Semantic Kernel, or AutoGen. Hands-on experience with Model Context Protocol (MCP) implementations. Strong understanding of vector databases and technologies such as Pinecone, Weaviate, FAISS, or Azure AI Search. Experience building and supporting Retrieval-Augmented Generation (RAG) solutions. Experience working with OpenAI and other modern AI platforms. Expertise ...

Aurora, Athena or other similar large scale database technologies. Experience working with AI framework such as LangChain/LlamaIndex Experience with Vector databases (Pinecone, Weaviate, FAISS) This is an outline description of the key responsibilities and accountabilities involved in the job. This is not an exhaustive list and the post ...

operating knowledge graphs or graph databases (e.g. Jena Fuseki, Neo4j, Amazon Neptune, or equivalent) Experience with vector databases and embedding pipelines (e.g. Pinecone, Weaviate, Qdrant, pgvector) Practical experience implementing RAG architectures or LLM-integrated data pipelines Familiarity with semantic web standards — JSON-LD, RDF, OWL, or SKOS Strong Python skills ...