24 of 24 Pinecone Jobs in the UK excluding London

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 ❯

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 ❯

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 ❯

up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP (Cloud Run, Vertex AI, BigQuery 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 ❯

based architectures such as GPT, BERT, or T5. Experience training and fine-tuning smaller deep learning models for NLP and computer vision tasks. Knowledge of vector databases (e.g., FAISS, Pinecone, Weaviate) and their use in AI-driven retrieval systems. Familiarity with anomaly detection techniques using deep learning and traditional ML approaches. Experience working with large-scale data processing frameworks (e.g. More ❯

Data Factory, Azure Synapse, and Azure Functions. Implementing modern retrieval techniques such as vector search, semantic search and Retrieval-Augmented Generation (RAG) using tools like Azure Cognitive Search, FAISS, Pinecone, or Weaviate. Familiarity with data governance, privacy, and ethical AI principles. Experience with DevOps tools and practices, including CI/CD pipelines, infrastructure as code and monitoring. Applied knowledge of More ❯

machine learning fundamentals , including supervised/unsupervised learning. Experience with cloud environments – ideally Azure , but AWS or GCP also considered. Familiarity with LLMs , prompt engineering , and vector databases (e.g. Pinecone, FAISS). Practical experience building production-ready AI applications. Ability to work on-site in Newcastle in a collaborative, agile environment. A curious mindset, eagerness to learn, and a genuine More ❯

machine learning fundamentals , including supervised/unsupervised learning. Experience with cloud environments – ideally Azure , but AWS or GCP also considered. Familiarity with LLMs , prompt engineering , and vector databases (e.g., Pinecone, FAISS). Practical experience building production-ready AI applications. Ability to work on-site in Sunderland in a collaborative, agile environment. A curious mindset, eagerness to learn, and a genuine More ❯

machine learning fundamentals , including supervised/unsupervised learning. Experience with cloud environments – ideally Azure , but AWS or GCP also considered. Familiarity with LLMs , prompt engineering , and vector databases (e.g. Pinecone, FAISS). Practical experience building production-ready AI applications. Ability to work on-site in Newcastle in a collaborative, agile environment. A curious mindset, eagerness to learn, and a genuine More ❯