1 to 25 of 40 Pinecone Jobs in the UK

Learn LangChain LangGraph Hugging Face FastAPI REST APIs Data Technologies SQL NoSQL Databases Data Modelling ETL Development Data Pipeline Engineering Vector Databases such as Pinecone, Weaviate or ChromaDB Cloud & DevOps Experience with at least one major cloud platform: AWS (preferred) Bedrock SageMaker Lambda ECS/EKS S3 Or experience within ...

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 ...

retrieval-augmented generation (RAG). Hands-on experience implementing GenAI solutions in production, including LLMs, prompt engineering, RAG architectures, LangChain, and vector databases (e.g., Pinecone, FAISS, Milvus). Proven hands on experience delivering and monitoring production grade AI/ML systems on cloud platforms such as AWS, GCP or Azure ...

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 ...

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 ...

regularisation, statistics Production ML experience—deploying, serving and monitoring models at scale LLM and generative AI tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation ...

experience with AI system design, architecture, and distributed systems Expertise in API-based backend development (e.g. FastAPI or similar) Experience with vector databases (e.g. Pinecone, Chroma) Experience with agent frameworks (e.g. LangChain, LangGraph, or similar) Strong understanding of evaluation frameworks for AI/agent systems Experience implementing CI/ ...

operate, support, and secure mission-critical applications Previous experience deploying and managing LLM-model based applications and agents Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols, including Retrieval ...

operate, support, and secure mission-critical applications Previous experience deploying and managing LLM-model based applications and agents Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols, including Retrieval ...

operate, support, and secure mission-critical applications Previous experience deploying and managing LLM-model based applications and agents Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols, including Retrieval ...

hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic frameworks (LangChain, LangGraph, Semantic Kernel). Data pipelines: data lake/lakehouse architectures, ETL/ELT, and data quality/lineage tooling ...

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 ...

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 ...

Microservices Architecture Cloud platforms - AWS, Azure, or GCP Preferred Agentic framework experience - LangChain, LangGraph, AutoGen, or similar RAG pipeline design and vector database experience (Pinecone, Weaviate, pgvector) Agile/SDLC tooling - Jira, Git, CI/CD pipelines At BNY, our culture allows us to run our company better and enables ...

Microservices Architecture Cloud platforms - AWS, Azure, or GCP Preferred Agentic framework experience - LangChain, LangGraph, AutoGen, or similar RAG pipeline design and vector database experience (Pinecone, Weaviate, pgvector) Agile/SDLC tooling - Jira, Git, CI/CD pipelines At BNY, our culture allows us to run our company better and enables ...

workflow orchestration using LangChain, CrewAI, AutoGen, ADK and LangGraph. Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search using vector databases such as Pinecone and Chroma, including indexing, retrieval policies, and evaluation. Build and integrate agent tools (MCP) and APIs to connect agents with external services, databases, and internal systems ...

workflow orchestration using LangChain, CrewAI, AutoGen, ADK and LangGraph. Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search using vector databases such as Pinecone and Chroma, including indexing, retrieval policies, and evaluation. Build and integrate agent tools (MCP) and APIs to connect agents with external services, databases, and internal systems ...

Mistral , including deployment via Azure OpenAI Service or Vertex AI . Implement retrieval-augmented generation (RAG) architectures and manage vector databases such as Pinecone , Weaviate , FAISS , and Milvus to support enterprise knowledge intelligence systems. Data Platform & Engineering Excellence Lead the evolution of the enterprise data estate, leveraging modern data platforms ...

extraction, policy summarisation, intelligent routing — including evaluation, guardrails, cost, and latency considerations. Architect retrieval-augmented generation (RAG) solutions using vector-search platforms (e.g., OpenSearch, Pinecone), owning decisions on chunking, embeddings, retrieval quality, and context management. Champion fluent use of AI coding assistants (Claude Code, GitHub Copilot, Cursor, etc.) across ...

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 ...

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 ...

/ML concepts and protocols, including Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP) Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Previous experience deploying and managing ML models Experience working in highly regulated environments or industries #ICBCareer #ICBEngineering ...

/ML concepts and protocols, including Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP) Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Previous experience deploying and managing ML models Experience working in highly regulated environments or industries #ICBCareer #ICBEngineering ...

DevSecOps practices. Set Yourself Apart... Cloud certifications (e.g. Azure Solutions Architect, Google Professional Cloud Architect). Exposure to MLOps frameworks and vector databases (e.g. Pinecone, Weaviate). Experience contributing to proof‐of‐concepts or MVPs, demonstrating rapid learning and experimentation. Experience supporting production AI/ML solutions, ideally within retail ...

Science, Artificial Intelligence, Machine Learning, or a strongly related quantitative field Hands-on experience with cloud-native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Experience with A/B testing and experimentation frameworks for AI features ...