17 of 17 Weaviate Jobs in the UK

. Hands-on experience with LLM frameworks and APIs (OpenAI, HuggingFace Transformers, LangChain, LlamaIndex). Experience building RAG pipelines with tools such as FAISS , Weaviate , Qdrant , or Pinecone . Solid knowledge of graph databases (Neo4j, TigerGraph) and knowledge graph construction . Proficiency in Python , with deep experience in data science More ❯

and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or More ❯

and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or More ❯

retrieval pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely More ❯

retrieval pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely More ❯

NLP, and conversational AI (e.g., GPT, Claude, Mistral, LLaMA, etc.) Expertise with frameworks like LangChain, Hugging Face, OpenAI, RAG pipelines, and vector databases (e.g., Weaviate, Pinecone, Chroma) Solid knowledge of AI system architecture, including model serving, monitoring, and optimization Strong programming skills in Python, with experience building APIs and integrating More ❯

with SageMaker, Bedrock, Lambda, S3, ECS, EKS Full-Stack Integration: Build APIs (FastAPI, Flask) and integrate with React, TypeScript, Node.js Vector Search: Use FAISS, Weaviate, Pinecone, ChromaDB, OpenSearch Required skills & experience: 3–5+ years of experience in ML engineering and software development Deep Python proficiency, with PyTorch, TensorFlow or Hugging More ❯

IOI, Top Coder, Kaggle). Technical Skills : Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch), Generative AI technologies, and libraries like Langchain, Weaviate, Langgraph, LlamaIndex. Track Record : Demonstrated success in applying data science and machine learning to solve real-world, business-critical problems. Collaboration : Excellent communication and collaboration More ❯

IOI, Top Coder, Kaggle). Technical Skills : Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch), Generative AI technologies, and libraries like Langchain, Weaviate, Langgraph, LlamaIndex. Track Record : Demonstrated success in applying data science and machine learning to solve real-world, business-critical problems. Collaboration : Excellent communication and collaboration More ❯

Deep knowledge of cloud infrastructure and services. Experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers). Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS). Experience with RAG (Retrieval-Augmented Generation), LLMOps, or LangChain/LlamaIndex. Proficiency with Infra-as-Code tools like Terraform. Understanding of data pipelines More ❯

based GenAI solutions, including RAG pipelines, prompt engineering, and integration with internal APIs. Help orchestrate tools using OpenAI function calling, vector databases (e.g. Pinecone, Weaviate), and knowledge graphs. Collaborate on the deployment of AI agents as secure microservices using Azure Kubernetes Services (AKS) and monitor system performance. Draft structured documentation More ❯

based GenAI solutions, including RAG pipelines, prompt engineering, and integration with internal APIs. Help orchestrate tools using OpenAI function calling, vector databases (e.g. Pinecone, Weaviate), and knowledge graphs. Collaborate on the deployment of AI agents as secure microservices using Azure Kubernetes Services (AKS) and monitor system performance. Draft structured documentation More ❯

solving skills and ability to work in a fast-paced, agile environment. Nice-to-Have Skills: Hands-on experience with Vector Databases (FAISS, Pinecone, Weaviate, PGVector, etc.). Experience fine-tuning LLMs for domain-specific applications. Knowledge of data privacy, governance, and compliance in AI-driven systems. Previous work in More ❯

hands-on experience developing and deploying AI/ML models. RAG Expertise: Experience in Retrieval-Augmented Generation and related vector databases (e.g., Pinecone, FAISS, Weaviate). LLMs & NLP: Experience working with LLMs (OpenAI, Anthropic, Hugging Face, etc.), including model tuning, security, and optimization. Regulatory Knowledge: Understanding of AI governance, compliance More ❯

extending an *AWS stack (Terraform, ECS Fargate, ALB, Secrets Manager, KMS)*. •⁠ ⁠Hands-on with *LLM APIs* and at least one *vector database* (Pinecone, Weaviate, OpenSearch, etc.). •⁠ ⁠Multi-tenant data design with GDPR awareness. •⁠ ⁠CI/CD and automated-testing mindset. Nice-to-Haves •⁠ ⁠*Solution-architect background*—ability to More ❯

we use it to power our analytics) OCR engines (we use AWS Textract, GDocAI, and we have used tesseractOCR in the past) Prompt Engineering Weaviate (we use it for RAG in LLM powered tasks and for hybrid searches) Kubernetes (we run Weaviate and other specific services on Kubernetes) CircleCI DataDog More ❯

strategies using vector/graph database technologies. Experience in developing ingestion workflows/pipelines for vector indexes using well-known providers (e.g., FAISS, Pinecone, Weaviate, Chroma). Experience with testing methodologies (unit/functional/e2e) and tools (unittest, pytest, etc.). Experience with agile development methodologies and version control … and experience in API design and implementation. Experience in developing ingestion workflows/pipelines for vector indexes using well-known providers (e.g., FAISS, Pinecone, Weaviate, Chroma). Build and deploy robust AI applications using technologies such as Llamanindex and LangChain for seamless orchestration of LLM (large language model) pipelines. Design More ❯