1 to 25 of 33 Permanent 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 ...

hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI).Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic frameworks (LangChain, LangGraph, Semantic Kernel).Data pipelines: data lake/lakehouse architectures, ETL/ELT, and data quality/lineage tooling that feed ...

platform (SageMaker, Azure ML, or Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic frameworks (LangChain, LangGraph, Semantic Kernel). Data pipelines: data lake/lakehouse architectures, ETL/ELT, and data quality/lineage tooling that feed ...

ability to explain technical concepts to both technical and non-technical stakeholders. Desirable Experience AI agents and agentic workflows. Vector databases such as Pinecone, Weaviate, or pgvector. Feature stores including Feast or AWS Feature Store. Docker, Kubernetes, or Amazon ECS. Infrastructure as Code using Terraform or CloudFormation. Big data processing ...

infrastructure, including related tools, design patterns, best practices etc.* Experience with vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate) and information retrieval systems and methods.* Familiarity with media content (video, audio) ML pipelines.* Proficiency in Python and common AI/ML libraries (e.g., PyTorch, Hugging Face Transformers, LangChain) and cloud frameworks ...

Ability to translate complex technical concepts into business value Desirable Experience Advanced LLM techniques including agents and autonomous workflows Vector databases such as Pinecone, Weaviate or pgvector Docker, Kubernetes, ECS or containerised deployments Infrastructure as Code tools including Terraform or CloudFormation Large-scale data processing technologies such as Spark ...

approaches are more suitable Desirable Experience Advanced LLM techniques, including agents, tool use and agentic workflows Experience with vector databases (e.g. Pinecone, Weaviate, pgvector) Feature store technologies such as Feast or AWS Feature Store Containerisation and orchestration using Docker, Kubernetes or ECS Infrastructure as Code using Terraform or CloudFormation Large ...

. Modern AI stack depth: Python/TypeScript, PyTorch, an orchestration framework (LangGraph, LangChain/LlamaIndex, CrewAI, or AutoGen), and vector databases (Pinecone, Qdrant, Weaviate, or Milvus) Model‐serving infrastructure: For example Triton/vLLM as well as LLM gateway products such as LiteLLM or Bifrost A production track record ...

. Modern AI stack depth: Python/TypeScript, PyTorch, an orchestration framework (LangGraph, LangChain/LlamaIndex, CrewAI, or AutoGen), and vector databases (Pinecone, Qdrant, Weaviate, or Milvus) Model-serving infrastructure: For example Triton/vLLM as well as LLM gateway products such as LiteLLM or Bifrost A production track record ...

skills with the ability to explain technical concepts to both technical and non-technical stakeholders.Desirable ExperienceAI agents and agentic workflows.Vector databases such as Pinecone, Weaviate, or pgvector.Feature stores including Feast or AWS Feature Store.Docker, Kubernetes, or Amazon ECS.Infrastructure as Code using Terraform or CloudFormation.Big data processing technologies such as Spark ...

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 ...

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 ...

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 ...

consistently across teams.• Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination … Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI/AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.• Architecture Design: Ability to design end-to-end Generative and Agentic ...

agent orchestration frameworks. Desirable: Background in AI reasoning, planning, or multi-agent systems. Desirable: Familiarity with transformer architecture and vector database technologies (Pinecone, Weaviate, Chroma). Desirable: Contributions to open-source AI/agentic projects. Degree in a numerical degree preferred. What’s in it for you? Competitive salary. ...

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 ...

Computer Science, Artificial Intelligence, Machine Learning, or a strongly related quantitative fieldHands-on experience with cloud-native ML infrastructure platformsKnowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding modelsExperience with model serving frameworks (vLLM, TensorRT, Ray)Experience with A/B testing and experimentation frameworks for AI featuresContributions to open ...

ideally in a regulated environment. Preferred qualifications:Background in financial services or another regulated enterprise environment. Experience with vector databases (for example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such ...

containerized deployments, CI/CD, and MLOps tooling (MLFlow, Airflow)Experience with Microsoft Agent Framework, Azure AI Agent ServiceKnowledge of vector databases (Pinecone, Weaviate, Qdrant, Milvus)Familiarity with guardrail and AI safety techniques (output filtering, prompt injection defense)Experience in distributed systems, event-driven architectures, and workflow enginesPrior involvement ...

agent workflows and autonomous AI systems Knowledge of the Model Context Protocol (MCP) and AI integration patterns Experience with vector databases such as Pinecone, Weaviate, FAISS, or Azure AI Search Experience working with OpenAI and other leading foundation models Knowledge of real-time AI pipelines, including speech-to-text ...

Engineer or Senior DeveloperHas breadth of experience across different tech-stacks, such as JavaScript/TypeScript, Java, C#, or Python, amongst othersExperienced in RAG Weaviate, OpenAI, Agents, MCP, Langchain, Prompt Engineering, Browser-Use, and DjangoExperienced building AI Tools, Agents and Agentic workflows (LangGraph, CrwAI)Hands-on Product building experience that ...

weight models) with tools, data, and external systems through MCP and custom connectors. Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector),hybrid retrieval with ElasticSearch or Solr, and BM25 + similarity search. Work across relational, document, key-value, and graph stores as the problem demands ...

ideally in a regulated environment. Preferred qualifications Background in financial services or another regulated enterprise environment. Experience with vector databases (for example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such ...

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 ...