5 of 5 Remote Weaviate Jobs in the City of London

curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development ...

clearly. Eligible for DV security clearance; no sponsorship provided. Desirable: Advanced LLM techniques: agents, tool use, and agentic workflows. Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications. Experience with feature stores (Feast, AWS Feature Store). Containerisation (Docker) and orchestration (Kubernetes, ECS). Infrastructure as Code (Terraform, CloudFormation ...

agent frameworks. Skilled in prompt engineering , integrating foundation model APIs, and LLM fine-tuning techniques. Expertise in building RAG systems , vector databases (e.g., Pinecone, Weaviate), and embedding pipelines. Experience with ML/LLMOps for monitoring, evaluation, and traceability. Solid understanding of distributed systems , microservices, and real-time data processing. Comfortable ...

APIs (OpenAI, Anthropic, Cohere) and cutting-edge open-source AI frameworks (LangChain, LlamaIndex) Design and optimise RAG pipelines leveraging embeddings and vector databases (Pinecone, Weaviate, FAISS) Develop advanced agentic workflows and harness knowledge graphs for smarter automation What we're looking for: Backend engineer with deep interest in practical ...

APIs (OpenAI, Anthropic, Cohere) and open-source AI frameworks (LangChain, LlamaIndex, LangGraph) into production Design and optimise RAG pipelines using embeddings, vector databases (Pinecone, Weaviate, FAISS) and other retrieval systems Work with knowledge graphs and structured data to add richer context and smarter reasoning to AI workflows Develop agentic/… practical, product-focused AI integration Experience building semantic search, RAG pipelines or similar retrieval-heavy AI systems Hands-on with vector databases (Pinecone, Weaviate, FAISS or similar) and large, complex datasets Familiarity with LLM tooling (e.g. LangChain, LlamaIndex) and agentic AI concepts Comfortable in modern backend stacks (Node.js, Python ...