23 of 23 Pinecone Jobs in the UK

Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong experience with Appian low-code platform including process modelling, interface design, expression More ❯

Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform- Strong experience with Appian low-code platform including process modelling, interface design, expression 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 ❯

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 ❯

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 ❯

and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data engineering. Familiarity with vector databases (FAISS, Pinecone, ChromaDB). Hands-on with Linux, Bash/Powershell scripting, cloud environments. Creative problem-solver with excellent communication and collaboration skills. Curious, adaptable, and passionate about staying at the edge 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

and AI agents) Build and maintain APIs, data pipelines, and backend components (primarily Python, FastAPI, or Flask) Engineer robust, containerised architectures using Docker and Kubernetes Work with ElasticSearch, Weaviate, Pinecone, and other vector databases Help deliver high-availability, fault-tolerant systems for mission-critical workloads What Were Looking For Degree in Computer Science, AI, or related field, or equivalent hands More ❯

or MongoDB, and a good understanding of data consistency tradeoffs. Proven knowledge of cloud platforms (e.g., AWS, Azure, or GCP). Bonus Experience with graph databases such as neo4j, Pinecone, Milvus or similar. Experience building native desktop apps. Experience with NLP libraries and frameworks, such as spaCy, or Transformers. Familiarity with machine learning concepts and the ability to work with More ❯

and serverless workflows for scalable deployment. ML/LLM Engineering Work with AI+ML pipelines throughAzure ML,AWS SageMaker,Vertex AI,Databricks, orModal/Fly.iofor lightweight LLM deployment. Utilizevector databases(Pinecone, Weaviate, Milvus, ChromaDB, pgVector) and embedding stores. UseAI-powered dev tools(GitHub Copilot, Cursor, Codeium, Aider, Windsurf) to accelerate iteration. ImplementLLMOps/PromptOpsusing: Weights & Biases,MLflow,LangSmith,LangFuse,PromptLayer,Humanloop … skills, with experience usingTransformers,LangChain,LlamaIndex and the broader GenAI ecosystem. Deep understanding of LLM behavior, prompt optimization, embeddings, retrieval and data preparation workflows. Experience with vector DBs (FAISS, Pinecone, Milvus, Weaviate, ChromaDB). Hands-on knowledge of Linux, Bash/Powershell, containers and cloud environments. Strong communication skills, creativity and a systems-thinking mindset. Curiosity, adaptability and a drive 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 … Bicep/Terraform). Data & Information Management Experience with relational/semi structured database (MS SQL and Cosmos DB) and vector search indexing (Azure AI Search/pgvector/Pinecone/Weaviate/Milvus/Qdrant) plus Neo4j or equivalent graph database. Software Engineering & Architecture Solid grasp of SDLC practices: unit/integration/E2E testing, code review, documentation, and More ❯

Open Banking APIs (TrueLayer, Yapily, etc.) to securely connect and manage financial data. Design and implement pipelines to analyse transaction data and user conversations using embeddings and vector databases (Pinecone, Qdrant, etc.). Ensure best practice in data security and compliance, including OAuth2, GDPR, and encrypted storage. Collaborate closely with founders and product to shape features, scale infrastructure, and make … integrating Open Banking APIs (TrueLayer, Yapily, Salt Edge, etc.). Hands-on experience with AI/LLM-powered systems — especially embedding models (OpenAI, Cohere, Sentence Transformers) and vector databases (Pinecone, Weaviate, FAISS, Chroma). Strong understanding of secure data practices and compliance standards. Excellent communicator — able to translate complex technical ideas to non-technical founders. Calm, methodical, and collaborative — a … with occasional meetups in London. Work alongside founders who value autonomy, communication, and long-term impact over quick fixes. Tech Stack Snapshot React Native • Node.js/FastAPI • TypeScript • PostgreSQL • Pinecone/Qdrant • OpenAI/Cohere APIs • TrueLayer/Yapily (Open Banking) • AWS This role suits someone who’s built apps end-to-end, thrives on ownership, and is ready to More ❯

Field Solutions Architect, GenAI, Google Cloud Google London, UK Responsibilities Advise our customers by understanding the customer's business process and objectives. Architect AI driven, spanning Data, AI, and Infrastructure, and work with peers to include the full cloud stack More ❯