26 to 50 of 62 Pinecone Jobs in London

within a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). More ❯

within a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). More ❯

security, and governance standards. Prepare and 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 of a strategic AI adoption roadmap More ❯

security, and governance standards. Prepare and 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 of a strategic AI adoption roadmap More ❯

security, and governance standards. Prepare and 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 of a strategic AI adoption roadmap More ❯

security, and governance standards. Prepare and 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 of a strategic AI adoption roadmap More ❯

security, and governance standards. Prepare and 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 of a strategic AI adoption roadmap More ❯

security, and governance standards. Prepare and 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 of a strategic AI adoption roadmap More ❯

semi-structured sources. Design RAG systems : chunking strategies, document schemas, metadata, hybrid/dense retrieval, re-ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search , pgvector, Pinecone/Weaviate). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners’ research and product offerings, including document understanding, automatic summarisation, topic modelling, semantic More ❯

within a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). More ❯

within a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). More ❯

within a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). More ❯

within a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). More ❯

Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and Ansible Experience with business process automation (RPA More ❯

party APIs and services within AI workflows. AI Frameworks: Familiarity with AI development frameworks such as LangChain or LangGraph. Vector Databases: Understanding of or experience with vector databases (e.g., Pinecone, Weaviate) for managing embeddings. Cloud Platforms: Familiarity with cloud services for deploying and managing applications (e.g., Google Cloud Run, AWS Lambda) is a plus. Our generous benefits package includes: Hybrid More ❯

party APIs and services within AI workflows. AI Frameworks: Familiarity with AI development frameworks such as LangChain or LangGraph. Vector Databases: Understanding of or experience with vector databases (e.g., Pinecone, Weaviate) for managing embeddings. Cloud Platforms: Familiarity with cloud services for deploying and managing applications (e.g., Google Cloud Run, AWS Lambda) is a plus. Our generous benefits package includes: Hybrid More ❯

party APIs and services within AI workflows. AI Frameworks: Familiarity with AI development frameworks such as LangChain or LangGraph. Vector Databases: Understanding of or experience with vector databases (e.g., Pinecone, Weaviate) for managing embeddings. Cloud Platforms: Familiarity with cloud services for deploying and managing applications (e.g., Google Cloud Run, AWS Lambda) is a plus. Our generous benefits package includes: Hybrid More ❯

party APIs and services within AI workflows. AI Frameworks: Familiarity with AI development frameworks such as LangChain or LangGraph. Vector Databases: Understanding of or experience with vector databases (e.g., Pinecone, Weaviate) for managing embeddings. Cloud Platforms: Familiarity with cloud services for deploying and managing applications (e.g., Google Cloud Run, AWS Lambda) is a plus. Our generous benefits package includes: Hybrid More ❯

ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to the creation of a strategic AI adoption roadmap and More ❯

/TigerGraph/Neptune or RDF/OWL; Cypher/SPARQL/GSQL ). Delivered GraphRAG with evidence it beats vector-only. Python + LangChain/LlamaIndex; vector stores (Pinecone/Chroma/LanceDB). Cloud delivery (AWS/Azure/GCP) with basic IaC/CI/CD and governance. Nice-to-have Text-to-Cypher/SPARQL with More ❯

/TigerGraph/Neptune or RDF/OWL; Cypher/SPARQL/GSQL ). Delivered GraphRAG with evidence it beats vector-only. Python + LangChain/LlamaIndex; vector stores (Pinecone/Chroma/LanceDB). Cloud delivery (AWS/Azure/GCP) with basic IaC/CI/CD and governance. Nice-to-have Text-to-Cypher/SPARQL with More ❯

/TigerGraph/Neptune or RDF/OWL; Cypher/SPARQL/GSQL ). Delivered GraphRAG with evidence it beats vector-only. Python + LangChain/LlamaIndex; vector stores (Pinecone/Chroma/LanceDB). Cloud delivery (AWS/Azure/GCP) with basic IaC/CI/CD and governance. Nice-to-have Text-to-Cypher/SPARQL with More ❯

/TigerGraph/Neptune or RDF/OWL; Cypher/SPARQL/GSQL ). Delivered GraphRAG with evidence it beats vector-only. Python + LangChain/LlamaIndex; vector stores (Pinecone/Chroma/LanceDB). Cloud delivery (AWS/Azure/GCP) with basic IaC/CI/CD and governance. Nice-to-have Text-to-Cypher/SPARQL with More ❯

Model Serving: Triton Inference Server, Hugging Face Inference Endpoints API Integration: OpenAI, Anthropic, Cohere, Mistral APIs LLM Frameworks: LangChain, LlamaIndex – for building LLM-powered applications Vector Databases: FAISS, Weaviate, Pinecone, Qdrant (Nice-to-Have) Retrieval-Augmented Generation (RAG): Experience building hybrid systems combining LLMs with enterprise data With a focus within Energy Trading, Oil & Gas, Financial Markets and Commodities, we More ❯

Model Serving: Triton Inference Server, Hugging Face Inference Endpoints API Integration: OpenAI, Anthropic, Cohere, Mistral APIs LLM Frameworks: LangChain, LlamaIndex – for building LLM-powered applications Vector Databases: FAISS, Weaviate, Pinecone, Qdrant (Nice-to-Have) Retrieval-Augmented Generation (RAG): Experience building hybrid systems combining LLMs with enterprise data With a focus within Energy Trading, Oil & Gas, Financial Markets and Commodities, we More ❯