1 to 25 of 33 Weaviate Jobs in the UK

scikit-learn; DL frameworks TensorFlow/PyTorch). Experience with LLM/RAG toolchains (e.g., LangChain, LlamaIndex, Semantic Kernel) and vector search (e.g., Pinecone, Weaviate, FAISS, Azure AI Search). Hands‐on experience with AWS Bedrock and Sagemaker would be highly desirable Experience with GenAI platforms (e.g., OpenAI API, Anthropic ...

LLMs (such as GPT, Claude, and Llama), and prompt engineering. Strong knowledge of RAG, embeddings, and vector databases (such as FAISS, Pinecone, ChromaDB, or Weaviate). Experience with LangChain, LangGraph, Semantic Kernel, or similar frameworks. Solid understanding of NLP, machine learning, and deep learning concepts. Hands‐on experience with Azure ...

APIs and frameworks (e.g. OpenAI/Anthropic SDKs, LangChain, LlamaIndex, or similar) Solid understanding of RAG architectures, embeddings, and vector databases (pgvector, Pinecone, Weaviate, Qdrant, or similar) Experience building APIs with FastAPI or Django; async Python is a plus Strong PostgreSQL skills; pgvector experience is a plus Familiarity with prompt ...

help you with them: Experience with 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) Experience with containerisation (Docker) and orchestration (Kubernetes, ECS) Familiarity with Infrastructure as Code (Terraform, CloudFormation) Experience with ...

help you with them: Experience with 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) Experience with containerisation (Docker) and orchestration (Kubernetes, ECS) Familiarity with Infrastructure as Code (Terraform, CloudFormation) Experience with ...

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 ...

Security First" approach to the software development lifecycle (SDLC). Desirable: AI & Data Underpinnings Vector Databases: Experience with high-dimensional data retrieval using Milvus, Weaviate, or Pinecone (essential for RAG architectures). Data Orchestration: Familiarity with Airflow, Prefect, or Dagster for managing complex data movement and model training pipelines. Search ...

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 ...

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 ...

have experience with: Agentic AI and multi-agent systems. LangChain, LangGraph or similar orchestration frameworks. Retrieval-Augmented Generation (RAG). Vector Databases (pgvector, Pinecone, Weaviate, Milvus, etc.). Model Context Protocol (MCP). FastAPI or modern Python backend frameworks. CI/CD and MLOps practices. Building production AI systems ...

open‐weight models) with tools, data, and external systems through MCP and custom connectors. Apply RAG techniques where they actuallyhelp:vector databases (Pinecone, Chroma,Weaviate,pgvector), hybrid retrieval withElasticSearchorSolr, and BM25 + similarity search. Work across relational, document, key‐value, and graph stores as the problem demands; use event‐driven ...

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 ...

APIs or services, with attention to latency, cost, scalability, and reliability Design and implement RAG (Retrieval‐Augmented Generation) systems using vector databases (Pinecone, Weaviate, Chroma), including embedding, chunking, and retrieval‐quality evaluation Integrate LLM APIs (OpenAI, Anthropic, etc.) into production services and product workflows Design and build agentic AI systems … Anthropic; LangChain or LlamaIndex Agentic AI – LangGraph, LangChain Agents, AutoGen, or CrewAI; tool/function calling, orchestration, memory, and guardrails RAG & vector databases —-Pinecone, Weaviate, or Chroma Databases – PostgreSQL, MongoDB, or Firebase Cloud & deployment – AWS, GCP, or Azure; Docker; CI/CD Security & compliance – secure coding, secrets management, access controls ...

Experience building or supporting RAG (Retrieval-Augmented Generation) architectures. Strong understanding of LLMs and production GenAI workloads. Vector database/search experience with Pinecone, Weaviate, pgvector or Azure AI Search . Experience with LangChain, LlamaIndex and/or MCP (Model Context Protocol) . LLM observability/evaluation experience using LangSmith ...

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 ...

Preferred Qualifications*** Familiarity with AI ecosystems such as LangGraph, Semantic Kernel, OpenAI, Azure OpenAI, or Anthropic, and/or vector stores such as Pinecone, Weaviate, OpenSearch, or PostgreSQL pgvector.* Experience building AI-powered applications in regulated industries, including financial services or trading environments.* Knowledge of enterprise architecture frameworks, governance models ...

Preferred Qualifications Familiarity with AI ecosystems such as LangGraph, Semantic Kernel, OpenAI, Azure OpenAI, or Anthropic, and/or vector stores such as Pinecone, Weaviate, OpenSearch, or PostgreSQL pgvector. Experience building AI-powered applications in regulated industries, including financial services or trading environments. Knowledge of enterprise architecture frameworks, governance models ...

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 ...

treat developers as customers and measure whether they adopt what you build. Desirable Experience: Experience with RAG architectures and vector databases (for example Pinecone, Weaviate, pgvector, Azure AI Search). Familiarity with agent and orchestration frameworks such as LangChain, LlamaIndex or the Model Context Protocol (MCP). LLM observability ...

cost optimisation Scalable cloud-based AI deployment Experience with technologies such as LangGraph, LangChain, LlamaIndex, AutoGen, Google Vertex AI, Gemini, BigQuery, Qdrant, ChromaDB, Pinecone, Weaviate or Milvus would be valuable. Network Programmability Hands-on network programmability experience is not essential , but you should understand the technologies and tools likely ...

cost optimisation Scalable cloud-based AI deployment Experience with technologies such as LangGraph, LangChain, LlamaIndex, AutoGen, Google Vertex AI, Gemini, BigQuery, Qdrant, ChromaDB, Pinecone, Weaviate or Milvus would be valuable. Network Programmability Hands-on network programmability experience is not essential , but you should understand the technologies and tools likely ...

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 ...

engineering, solutions engineering, or ML deployment role Proficiency in Python and working knowledge of AI/ML frameworks (e.g., OpenAI, Hugging Face, LangChain, Pinecone, Weaviate) Familiarity with building LLM applications, RAG systems, or conversational AI workflows Experience integrating with enterprise systems via APIs or middleware (e.g., Salesforce, ServiceNow, SAP, Oracle ...

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 ...

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 ...