15 of 15 Pinecone Jobs in the UK excluding London

FastAPI) or TypeScript (Express). Create front-end applications with React, TypeScript, and frameworks like Next.js or Vite. Integrate LLMs (e.g., OpenAI, Anthropic, Mistral) and vector databases (e.g., ChromaDB, Pinecone, PGVector). Deploy solutions using AWS or Azure, Docker, Kubernetes, and Terraform. Implement CI/CD pipelines and monitor LLM performance using tools like Langsmith or Langfuse. Collaborate in agile 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 ❯

Essential Skills & Experience 2-6 years of Python development experience Hands-on experience with Generative AI frameworks (LangChain, LlamaIndex, Haystack) and LLM APIs Knowledge of prompt engineering, vector databases (Pinecone, ChromaDB, FAISS), and model fine-tuning Cloud deployment experience with Docker/Kubernetes Understanding of transformer architectures, AI/ML principles, and MLOps Experience developing REST APIs Desirable Exposure to More ❯

practices and high-quality outputs. Skills & Experience 6+ years of Python development with hands-on experience in Generative AI Strong experience with LLM APIs, prompt engineering, and vector databases (Pinecone, ChromaDB, FAISS) Proven team leadership and mentoring skills ; managing 3-5 engineers Cloud deployment experience with Docker/Kubernetes Deep understanding of transformer architectures, AI/ML principles, and MLOps 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 ❯

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 ❯

curating diverse training datasets (structured/unstructured text, images, code). Deep knowledge of data preprocessing, tokenization, and embedding generation techniques. Hands-on experience working with Vector Databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Stakeholder & Strategic Partnership: Ability to partner effectively with business stakeholders to identify, shape, and prioritize high-impact AI use cases. Experience contributing More ❯

the data science or ML community (libraries, notebooks, packages, or tutorials). Experience presenting research or applied work at meetups, workshops, or industry conferences. Familiarity with vector databases (FAISS, Pinecone, Milvus, Weaviate) and LLM application frameworks. Cloud or AI/ML certifications (e.g., AWS Machine Learning Specialty, Google Professional Data Engineer, Azure AI Engineer) are a plus. Benefits Work at More ❯

Infrastructure as Code (Terraform, CloudFormation), with additional experience in modern orchestration frameworks (Airflow, Prefect, or dbt). Proficiency with cloud-native data platforms (AWS, Azure, GCP) and vector databases (Pinecone, Weaviate, Qdrant, or Chroma). Experience with MLOps tools and platforms (HuggingFace, SageMaker Bedrock, Vertex AI), experiment tracking (MLflow, Weights & Biases), and model deployment pipelines. Experience with Large Language Models More ❯

Qualifications: Experience with real-time AI applications, recommendation systems, or personalization engines. Knowledge of AI ethics, model interpretability, and bias mitigation. Familiarity with production-scale vector databases (e.g., QDrant, Pinecone, Weaviate). Previous experience in a startup or fast-paced environment. Why Join Us? Opportunity to lead cutting-edge AI initiatives in a dynamic environment. Work with a team of 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 ❯

DevOps & tooling (Git, CI/CD, API integration), front-end & visualization (Streamlit, Gradio, React), and cloud environments (AWS/GCP/Azure). Experience with vector and graph databases (Pinecone, FAISS, Neo4j). Understanding of model evaluation metrics and automation techniques. Awareness of AI ethics and responsible AI practices. Bachelor's, Master's, or PhD in Computer Science, Engineering, or More ❯

A pragmatic, delivery-focused mindset — comfortable in fast-moving, evolving environments. The tech environment: Python, PyTorch/TensorFlow, HuggingFace Transformers Semantic search/Embeddings/Vector DBs (e.g., FAISS, Pinecone, Weaviate) ML Ops tools (MLflow/Weights & Biases) AWS/Docker/Kubernetes If you’re the Head of AI & Machine Learning we’re looking for then we want to More ❯

A pragmatic, delivery-focused mindset — comfortable in fast-moving, evolving environments. The tech environment: Python, PyTorch/TensorFlow, HuggingFace Transformers Semantic search/Embeddings/Vector DBs (e.g., FAISS, Pinecone, Weaviate) ML Ops tools (MLflow/Weights & Biases) AWS/Docker/Kubernetes If you’re the VP of AI & Machine Learning we’re looking for then we want to More ❯

experience with GraphRAG or Graph-based RAG architectures. Proficiency in Python and frameworks like LangChain, Semantic Kernel, or similar. Hands-on with graph databases (Neo4j, TigerGraph) and vector DBs (Pinecone, Weaviate, FAISS). Knowledge of LLMs, prompt engineering, and retrieval optimization. Familiarity with knowledge graph construction, entity linking, and graph algorithms. Must have full right to work and live in More ❯