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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯