1 to 25 of 45 Pinecone Jobs in the UK

ensuring reliable, secure, and consistent operation. Retrieval‐Augmented Generation (RAG) & Knowledge Architecture – expertise in RAG and knowledge‐grounded systems, including chunking, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search and retrieval evaluation. Familiarity with GraphRAG and agentic RAG. LLMOps, Evaluation & Responsible AI – operationalizing LLM and agentic systems ...

experience with AI orchestration and agent frameworks including LangChain, LangGraph, Semantic Kernel, LlamaIndex, CrewAI, and AutoGen. Strong knowledge of Prompt Engineering, RAG, Vector Databases (Pinecone, Weaviate, Chroma, FAISS, Azure AI Search), Model Fine-Tuning, LLMOps, and Secure AI practices. Enterprise Architecture & Security Proficiency in enterprise architecture frameworks (TOGAF, Zachman), cloud ...

collaborating effectively with technical and non-technical stakeholders.DesirableExperience with workflow automation platforms such as n8n, Power Automate, Dify or similar.Knowledge of vector databases including Pinecone, Weaviate, Chroma, FAISS or Milvus.Experience building Retrieval Augmented Generation (RAG) solutions.Understanding of AI governance, Responsible AI and AI security principles.Exposure to MLOps, model deployment ...

collaborating effectively with technical and non-technical stakeholders.DesirableExperience with workflow automation platforms such as n8n, Power Automate, Dify or similar.Knowledge of vector databases including Pinecone, Weaviate, Chroma, FAISS or Milvus.Experience building Retrieval Augmented Generation (RAG) solutions.Understanding of AI governance, Responsible AI and AI security principles.Exposure to MLOps, model deployment ...

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 ...

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 ...

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 ...

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 ...

embeddings, prompt engineering or fine tuning Exposure to LangChain, LangGraph, Agent Development Kit or similar agent frameworks Experience with vector databases such as Pinecone, Chroma or similar API development experience, ideally with FastAPI or similar frameworks Knowledge of MLOps, LLMOps, CI/CD or production deployment practices Experience designing ...

Experience with multi-agent systems or distributed AI architecturesExperience optimising LLM usage across multiple providers (cost/performance trade-offs)Familiarity with vector databases (Pinecone, Weaviate, OpenSearch, etc.)Experience integrating AI systems into enterprise platforms or business workflows Contract Details: £550 – £700 Per Day (Inside IR35)6 Months Initial Contract ...

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 ...

when non-AI approaches are more suitable Desirable Experience Advanced LLM techniques, including agents, tool use and agentic workflows Experience with vector databases (e.g. Pinecone, Weaviate, pgvector) Feature store technologies such as Feast or AWS Feature Store Containerisation and orchestration using Docker, Kubernetes or ECS Infrastructure as Code using Terraform ...

GitLab, Jenkins, or similar. Familiarity with monitoring and observability tools for AI applications and services. Database Technologies Experience with vector databases such as ChromaDB, Pinecone, PGVector, Qdrant, MongoDB, or similar solutions. Familiarity with NoSQL databases. Experience working with SQL databases, particularly PostgreSQL or equivalent. Version Control Strong experience using ...

GitLab, Jenkins, or similar. Familiarity with monitoring and observability tools for AI applications and services. Database Technologies Experience with vector databases such as ChromaDB, Pinecone, PGVector, Qdrant, MongoDB, or similar solutions. Familiarity with NoSQL databases. Experience working with SQL databases, particularly PostgreSQL or equivalent. Version Control Strong experience using ...

GitLab, Jenkins, or similar. Familiarity with monitoring and observability tools for AI applications and services. Database Technologies Experience with vector databases such as ChromaDB, Pinecone, PGVector, Qdrant, MongoDB, or similar solutions. Familiarity with NoSQL databases. Experience working with SQL databases, particularly PostgreSQL or equivalent. Version Control Strong experience using ...

Foundry or Microsoft Agent Framework Knowledge of MCP and A2A protocols for agent and tool interoperability Hands-on work with vector databases like Pinecone, Weaviate, Qdrant or pgvector Familiarity with distributed systems, workflow engines (Temporal, Airflow or Dagster) and event-driven architectures Experience with open-source LLMs or Small Language ...

learning concepts, embeddings, transformers, and LLM architectures.* Experience using LangChain, LangGraph, LlamaIndex, AutoGen, MCP, or similar AI frameworks.* Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, ChromaDB, or equivalent.* Experience with graph databases, knowledge graphs, or Graph APIs.* Experience building data pipelines and integrating structured and unstructured data ...

LlamaIndex, and Hugging Face, including agentic and multi‐step workflows Proven experience designing and implementing Retrieval‐Augmented Generation (RAG) systems, including vector databases (pgvector, Pinecone, Weaviate, FAISS, etc.) and advanced retrieval strategies (hybrid search, re‐ranking, metadata filtering) Strong understanding of prompt engineering, workflow design, and Gen‐AI optimization, balancing ...

dialogue management. Prompt engineering and Retrieval-Augmented Generation (RAG) . Real-time data streaming platforms such as Kafka . Vector databases including Pinecone, Milvus or Qdrant . Model compression and optimisation techniques including INT8/FP4 quantisation, pruning and knowledge distillation . Deploying machine learning models to resource-constrained ...

pipelines and integrating with enterprise systems (REST, Python, SQL, GraphQL, Webhooks etc). Solid understanding of LLMs, Prompt engineering/tuning, Vector DBs (AstraDB, Pinecone, Weaviate etc), RAG pipelines (LlamaIndex, Haystack) and Agent/Workflow Orchestration (LangChain, LangGraph, CrewAI). Knowledge of SLM Finetuning and Distillation is big advantage. Experience ...

technical vision, and delivering complex, cross‐team projects. English — advanced proficiency, written and spoken. Preferred Qualifications Fine‐tuning, RLHF, or model customization. Vector databases (Pinecone, Weaviate, or FAISS) and embedding retrieval. Building agent workflows with MCP servers or modern agent frameworks. Conversational AI and speech (speech‐to‐text/text ...

powered applications in enterprise SaaS or complex data platforms. AI & Engineering Mastery: Deep technical expertise in Python or Go, semantic search, vector databases (e.g., Pinecone, Milvus, pgvector), orchestration frameworks (LangChain, LlamaIndex), and fine-tuning or prompt engineering of state-of-the-art Large Language Models (LLMs). Extreme Ownership: High ...

tools/functions callable by AI agents (tool schemas, function calling, structured outputs). Experience with retrieval-augmented generation (RAG) : vector databases (e.g., Pinecone, Weaviate, pgvector), embeddings, and chunking strategies. Working knowledge of prompt engineering and evaluation techniques for agent reliability, safety, and cost control. Familiarity with orchestrating multi-agent ...

with a strong emphasis on Python development in production environments Proven expertise in building and deploying generative AI applications, leveraging LLMs, vector databases like Pinecone, Weaviate, or pgvector, and modern open-source agentic frameworks Practical experience with microservices architecture, RESTful APIs, cloud platforms such as AWS, GCP, or Azure ...

LangChain, Semantic Kernel, or AutoGen. Hands-on experience with Model Context Protocol (MCP) implementations. Strong understanding of vector databases and technologies such as Pinecone, Weaviate, FAISS, or Azure AI Search. Experience building and supporting Retrieval-Augmented Generation (RAG) solutions. Experience working with OpenAI and other modern AI platforms. Expertise ...