1 to 25 of 26 Weaviate Jobs in the UK

reliable, secure, and consistent operation. Retrieval‐Augmented Generation (RAG) & Knowledge Architecture – expertise in RAG and knowledge‐grounded systems, including chunking, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search and retrieval evaluation. Familiarity with GraphRAG and agentic RAG. LLMOps, Evaluation & Responsible AI – operationalizing LLM and agentic systems at scale ...

with AI orchestration and agent frameworks including LangChain, LangGraph, Semantic Kernel, LlamaIndex, CrewAI, and AutoGen. Strong knowledge of Prompt Engineering, RAG, Vector Databases (Pinecone, Weaviate, Chroma, FAISS, Azure AI Search), Model Fine-Tuning, LLMOps, and Secure AI practices. Enterprise Architecture & Security Proficiency in enterprise architecture frameworks (TOGAF, Zachman), cloud-native ...

with multi-agent systems or distributed AI architecturesExperience optimising LLM usage across multiple providers (cost/performance trade-offs)Familiarity with vector databases (Pinecone, Weaviate, OpenSearch, etc.)Experience integrating AI systems into enterprise platforms or business workflows Contract Details: £550 – £700 Per Day (Inside IR35)6 Months Initial Contract | + ...

concepts, embeddings, transformers, and LLM architectures.* Experience using LangChain, LangGraph, LlamaIndex, AutoGen, MCP, or similar AI frameworks.* Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, ChromaDB, or equivalent.* Experience with graph databases, knowledge graphs, or Graph APIs.* Experience building data pipelines and integrating structured and unstructured data sources. ...

with experience developing scalable APIs, AI services, and automation solutions.* Experience implementing vector search solutions using Vector Databases such as Azure AI Search, Pinecone, Weaviate, Chroma, Milvus, or similar technologies.* Experience working with Large Language Models (LLMs) such as GPT, Llama, Claude, Mistral, or equivalent foundation models.* Strong understanding ...

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 ...

vision, and delivering complex, cross‐team projects. English — advanced proficiency, written and spoken. Preferred Qualifications Fine‐tuning, RLHF, or model customization. Vector databases (Pinecone, Weaviate, or FAISS) and embedding retrieval. Building agent workflows with MCP servers or modern agent frameworks. Conversational AI and speech (speech‐to‐text/text ...

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 ...

Azure)* CI/CD pipelines* Scalable service architectureData and Retrieval Systems* Experience building and operating knowledge layers* Vector databases (e.g. Pinecone, Weaviate, pgvector)* Document ingestion pipelines* Embedding workflows* Search and retrieval optimization**Nice to Have Experience with:*** MCP architectures or tool-connected AI systems* Agent frameworks* Knowledge graph systems* Streaming ...

integrating with enterprise systems (REST, Python, SQL, GraphQL, Webhooks etc). Solid understanding of LLMs, Prompt engineering/tuning, Vector DBs (AstraDB, Pinecone, Weaviate etc), RAG pipelines (LlamaIndex, Haystack) and Agent/Workflow Orchestration (LangChain, LangGraph, CrewAI). Knowledge of SLM Finetuning and Distillation is big advantage. Experience and knowledge ...

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 ...

Foundry or Microsoft Agent Framework Knowledge of MCP and A2A protocols for agent and tool interoperability Hands-on work with vector databases like Pinecone, Weaviate, Qdrant or pgvector Familiarity with distributed systems, workflow engines (Temporal, Airflow or Dagster) and event-driven architectures Experience with open-source LLMs or Small Language ...

Artificial Intelligence, Machine Learning, or a strongly related quantitative field Hands‐on experience with cloud‐native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Experience with A/B testing and experimentation frameworks for AI features Contributions ...

Agentic frameworks: LangGraph, LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector Cloud platforms: AWS Bedrock, GCP Vertex AI or Azure AI Studio CI/CD for AI: version-controlled prompts, automated testing, deployment ...

Agentic frameworks: LangGraph, LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector Cloud platforms: AWS Bedrock, GCP Vertex AI or Azure AI Studio CI/CD for AI: version‐controlled prompts, automated testing, deployment ...

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 ...

Artificial Intelligence, Machine Learning, or a strongly related quantitative field Hands‐on experience with cloud‐native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Experience with A/B testing and experimentation frameworks for AI features Contributions ...

like Llama/Mistral) and orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent). Practical expertise in RAG design, embeddings, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search). Experience with agentic architectures, function/tool calling, and structured outputs. Solid prompt engineering, evaluation, and LLM guardrail/safety ...

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 ...

tools/functions callable by AI agents (tool schemas, function calling, structured outputs). Experience with retrieval-augmented generation (RAG) : vector databases (e.g., Pinecone, Weaviate, pgvector), embeddings, and chunking strategies. Working knowledge of prompt engineering and evaluation techniques for agent reliability, safety, and cost control. Familiarity with orchestrating multi-agent ...

strong emphasis on Python development in production environments Proven expertise in building and deploying generative AI applications, leveraging LLMs, vector databases like Pinecone, Weaviate, or pgvector, and modern open-source agentic frameworks Practical experience with microservices architecture, RESTful APIs, cloud platforms such as AWS, GCP, or Azure, and containerization with ...

. Strong understanding of system design, microservices, event-driven architecture and cloud-native development . Experience working with PostgreSQL, MongoDB, Vector Databases (Qdrant, Pinecone, Weaviate or similar) . Comfortable working in ambiguous environments where priorities evolve quickly. Product-minded engineer who wants to own outcomes, not just tickets. Nice ...

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 ...

practices (testing, code review, CI/CD). Preferred Skills Hands‐on experience with cloud‐native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Familiarity with Anaplan or similar enterprise planning platforms Experience with A/ ...

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 ...