1 to 25 of 31 Pinecone Jobs in the UK

custom LLM integrations). Exposure to AI ethics, data privacy, and compliance regulations. Prior experience in multi-agent systems or autonomous AI workflows. Hands-on experience with vector databases (Pinecone, Weaviate, FAISS) and AI embeddings. Remote WorkingSome remote working CountryUnited Kingdom LocationWC1 Job TypeContract or Permanent Start DateApr-Jul 25 Duration9 months initial or permanent Visa RequirementApplicants must be eligible More ❯

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 ❯

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 ❯

/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers , or TensorFlow. Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex). Hands-on experience with fine-tuning and distillation of large language models. Comfortable with cloud platforms (Azure preferred), CI/CD tools 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 ❯

for ML models (MLOps best practices). Example Tools & Technologies: Frameworks & Libraries: LangChain, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn Agentic AI Tools: OpenAI GPT models, Crew,AI, Cohere, Pinecone (for vector databases), AutoGPT Data Engineering & ML Pipelines: Apache Airflow, MLflow, Kubeflow, dbt, Prefect Cloud & Deployment Platforms: AWS SageMaker, Azure ML, Google Vertex AI APIs & Orchestration: OpenAI API, Anthropic Claude More ❯

controllers. Develop and maintain AI microservices using Docker, Kubernetes, and FastAPI, ensuring smooth model serving and error handling; Vector Search & Retrieval: Implement retrieval-augmented workflows: ingest documents, index embeddings (Pinecone, FAISS, Weaviate), and build similarity search features. Rapid Prototyping: Create interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/… experience fine-tuning LLMs via OpenAI, HuggingFace or similar APIs; Strong proficiency in Python; Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex; Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines; Proven rapid-prototyping skills using Streamlit or equivalent frameworks for UI demos. Familiarity with containerization (Docker) and at least one orchestration/deployment platform More ❯

Cloud & MLOps (AWS): Deploy with SageMaker, Bedrock, Lambda, S3, ECS, EKS Full-Stack Integration: Build APIs (FastAPI, Flask) and integrate with React, TypeScript, Node.js Vector Search: Use FAISS, Weaviate, Pinecone, ChromaDB, OpenSearch Required skills & experience: 3-5+ years of experience in ML engineering and software development Deep Python proficiency, with PyTorch, TensorFlow or Hugging Face Proven experience with LLMs More ❯

Cloud & MLOps (AWS): Deploy with SageMaker, Bedrock, Lambda, S3, ECS, EKS Full-Stack Integration: Build APIs (FastAPI, Flask) and integrate with React, TypeScript, Node.js Vector Search: Use FAISS, Weaviate, Pinecone, ChromaDB, OpenSearch Required skills & experience: 3-5+ years of experience in ML engineering and software development Deep Python proficiency, with PyTorch, TensorFlow or Hugging Face Proven experience with LLMs 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 ❯

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 ❯

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 ❯

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 ❯

large-scale infrastructure, and modern backend development using Java, Python, Golang, Spring Boot, Flask, and Kubernetes. We focus on integrating RAG-powered LLMs, implementing advanced vector search (FAISS, Milvus, Pinecone), and building scalable and high-performance AI-driven solutions. You Might Be a Good Fit If You: Have deep hands-on software engineering expertise in Java or Python Thrive in … applications using Java, Python, and modern backend frameworks Integrate LLMs into enterprise-scale systems using internal frameworks and libraries Design and implement vector search solutions using FAISS, Milvus, and Pinecone Build scalable APIs and backend services using Spring Boot, Flask, and FastAPI Optimize data storage and retrieval with PostgreSQL/MongoDB and distributed databases Deploy and manage cloud-native applications … Succeed in This Role: Proficiency in Java or Python for backend development Strong knowledge of Spring Boot, Flask, FastAPI, and API design Experience with vector search frameworks (FAISS, Milvus, Pinecone) Expertise in Kubernetes and Docker for scalable deployment Understanding of authentication & security frameworks (Spring Security, SSO) Hands-on experience with PostgreSQL and distributed storage Experience with Maven or Gradle for 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 ❯

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 ❯

developer tools, open-source culture, and improving developer workflows. Excellent communication and collaboration skills in a remote-first environment. Experience contributing to open-source AI projects. Experience with LangChain, Pinecone, or similar AI frameworks/infrastructure. Past experience building AI features into developer platforms or tools. Benefits Our entire company is distributed, so we take remote work seriously. If you More ❯

engineering experience, including significant time spent building data, ML, or backend systems Deep proficiency in Python and experience with ML/LLM frameworks such as Hugging Face, LangChain, OpenAI, Pinecone, etc. Familiarity with full-stack or API-based deployment patterns (Docker, FastAPI, Kubernetes, GCP/AWS) Strong product and system design instincts - you understand business needs and how to translate More ❯

Interest in or experience with Retrieval-Augmented Generation (RAG) systems. Strong communication skills and a collaborative, proactive mindset. Nice to Have: Experience with LLM pipelines and vector databases (e.g. Pinecone, FAISS). Familiarity with data versioning and experiment tracking tools (e.g. DVC, MLflow). Background in supporting AI/ML research teams or trading environments. On Offer: A role contributing More ❯