1 to 25 of 33 Pinecone Jobs in the UK excluding London

RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis, Azure AI Search) to support semantic search and recommendation systems. Building intelligent features like AI-powered chatbots , assistants , and question-answering systems using LLMs and conversational agents. Awareness of More ❯

Express, Next.js Integrate ML models and embeddings into production pipelines using AWS SageMaker , Bedrock or OpenAI APIs Build support systems for autonomous agents including memory storage, vector search (e.g., Pinecone, Weaviate) and tool registries Enforce system-level requirements for security, compliance, observability and CI/CD Drive PoCs and reference architectures for multi-agent coordination , intelligent routing and goal-directed … similar Experience with secure cloud deployments and production ML model integration Bonus Skills Applied work with multi-agent systems , tool orchestration, or autonomous decision-making Experience with vector databases (Pinecone, Weaviate, FAISS) and embedding pipelines Knowledge of AI chatbot frameworks (Rasa, BotPress, Dialogflow) or custom LLM-based UIs Awareness of AI governance , model auditing, and data privacy regulation (GDPR, DPA More ❯

technical stakeholders. Strong stakeholder management and project delivery experience across cross-functional teams. Preferred Qualifications Background in AI ethics, fairness, compliance, or regulatory frameworks. Familiarity with Salesforce, vector databases (Pinecone, Weaviate, FAISS), graph-based reasoning, or knowledge graphs. Experience in property maintenance, home services, or customer service automation (not required, but a bonus). Why Join Us? Be a key More ❯

and modern web frameworks Deep experience with AI/ML frameworks (PyTorch, TensorFlow, Transformers, LangChain) Mastery of prompt engineering and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS More ❯

and modern web frameworks Deep experience with AI/ML frameworks (PyTorch, TensorFlow, Transformers, LangChain) Mastery of prompt engineering and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS More ❯

CI/CD : Experience with continuous integration and deployment tools such as GitLab , GitHub , or Jenkins . Database Management Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Version Control Proficient in Git and version control platforms More ❯

similar. CI/CD: Experience with continuous integration and deployment tools such as GitLab, GitHub, or Jenkins. Database Management Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Version Control Proficient in Git and version control platforms More ❯

monitoring. Full-Stack Integration : Develop APIs 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 Hugging Face Transformers Experience More ❯

monitoring. Full-Stack Integration : Develop APIs 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 Hugging Face Transformers Experience More ❯

monitoring. Full-Stack Integration : Develop APIs 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 Hugging Face Transformers Experience More ❯

skills and ability to work in a team environment. Preferred Qualifications: Experience working with large-scale AI applications and personalization engines. Familiarity with production-scale vector databases (e.g., QDrant, Pinecone, Weaviate). Understanding of AI model interpretability and ethical AI considerations. Exposure to real-time AI applications and MLOps workflows. Why Join Us? Work alongside industry experts on cutting-edge More ❯

monitoring. Full-Stack Integration : Develop APIs 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 Hugging Face Transformers Experience with More ❯

management using frameworks such as LangChain, CrewAI, and Autogen. Engineer and tune prompts to enhance the performance and reliability of generative tasks. Design RAG systems using vector databases like Pinecone, Chroma, and PosgreSQL for contextual retrieval. Incorporate semantic search and embedding strategies for more relevant and grounded LLM responses. Utilize Guardrails to implement applications that adhere to responsible AI guidelines. More ❯

management using frameworks such as LangChain, CrewAI, and Autogen. Engineer and tune prompts to enhance the performance and reliability of generative tasks. Design RAG systems using vector databases like Pinecone, Chroma, and PosgreSQL for contextual retrieval. Incorporate semantic search and embedding strategies for more relevant and grounded LLM responses. Utilize Guardrails to implement applications that adhere to responsible AI guidelines. More ❯

CI/CD : Experience with continuous integration and deployment tools such as GitLab , GitHub , or Jenkins . Database Management Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB, Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Version Control Proficient in Git and version control platforms More ❯

CI/CD : Experience with continuous integration and deployment tools such as GitLab , GitHub , or Jenkins . Database Management Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Version Control Proficient in Git and version control platforms More ❯

CI/CD : Experience with continuous integration and deployment tools such as GitLab , GitHub , or Jenkins . Database Management Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Version Control Proficient in Git and version control platforms More ❯

CI/CD : Experience with continuous integration and deployment tools such as GitLab , GitHub , or Jenkins . Database Management Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Version Control Proficient in Git and version control platforms More ❯

CI/CD : Experience with continuous integration and deployment tools such as GitLab , GitHub , or Jenkins . Database Management Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Version Control Proficient in Git and version control platforms More ❯

varied use cases. Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. Fine-tune prompts to optimise performance, reliability, and alignment. Design and implement memory modules for short-term and long-term agent behaviours. Deploy models and orchestrate More ❯

varied use cases. • Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. • Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. • Fine-tune prompts to optimise performance, reliability, and alignment. • Design and implement memory modules for short-term and long-term agent behaviours. • Deploy models and orchestrate More ❯

in Python, ML frameworks (PyTorch, TensorFlow), and cloud-native AI solutions. Hands-on experience with Azure Cognitive Services, OpenAI APIs, and vector search (e.g., Azure AI Search, FAISS, CosmosDB, Pinecone, etc.). Experience in AI security, MLOps, and deploying scalable AI solutions. Ability to troubleshoot and optimize AI models for performance and accuracy About Capgemini Capgemini is a global business More ❯

LLM/agent-based prototypes (e.g., copilots, chatbots, A2A agents). Implement multi-step reasoning, memory modules, and RAG pipelines. Use frameworks like LangChain, LangGraph, CrewAI, and tools like Pinecone, FAISS. Optimize performance and ensure responsible AI practices. Deploy via cloud platforms (AWS Bedrock, Azure AI, Google Vertex). Build UIs (Streamlit, Gradio, React) and integrate APIs and databases. Preferred More ❯

expertise to support advanced analytical use cases and ML, AI opportunities. Experience with containerisation technologies ( Docker, Kubernetes ) for scalable data solutions. Experience with vector databases and graph databases (e.g., Pinecone, Neo4j, AWS Neptune ). Understanding of data mesh-fabric approaches and modern data architecture patterns . Familiarity with AI/ML workflows and their data requirements. Experience with API specifications More ❯

day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices More ❯