1 to 25 of 34 Pinecone Jobs in the UK

least one 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 ...

ability to explain technical concepts to both technical and non-technical stakeholders. Desirable Experience AI agents and agentic workflows. Vector databases such as Pinecone, Weaviate, or pgvector. Feature stores including Feast or AWS Feature Store. Docker, Kubernetes, or Amazon ECS. Infrastructure as Code using Terraform or CloudFormation. Big data processing ...

skills Ability to translate complex technical concepts into business value Desirable Experience Advanced LLM techniques including agents and autonomous workflows Vector databases such as Pinecone, Weaviate or pgvector Docker, Kubernetes, ECS or containerised deployments Infrastructure as Code tools including Terraform or CloudFormation Large-scale data processing technologies such as Spark ...

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

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 ...

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 ...

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 ...

memory, and 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 ...

tools/functions callable by AI agents (tool schemas, function calling, structured outputs). Experience with retrieval-augmented generation (RAG): vector databases (eg, 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 ...

knowledge graphs for context enrichment, reasoning, and workflow automationRAG & Knowledge Systems: End-to-end RAG pipelines using enterprise search + vector stores (e.g., Elastic, Pinecone) with grounding, evaluation, and optimizationModel Lifecycle & Governance: Model evaluation, monitoring, prompt/version control, and Responsible AI/MRM complianceEnterprise AI Integration & DataAPI- and event ...

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 ...

communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.Desirable ExperienceAI agents and agentic workflows.Vector databases such as Pinecone, Weaviate, or pgvector.Feature stores including Feast or AWS Feature Store.Docker, Kubernetes, or Amazon ECS.Infrastructure as Code using Terraform or CloudFormation.Big data processing technologies such ...

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 ...

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 ...

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 ...

REST/SDK — for use cases such as document extraction, policy summarisation, or intelligent routing. Build or consume vector-search capabilities (e.g., OpenSearch, Pinecone) for retrieval-augmented generation (RAG) patterns. Use AI coding assistants (Claude Code, GitHub Copilot, Cursor, etc.) fluently as part of your daily development workflow to raise ...

Gemini, open-weight models) with tools, data, and external systems through MCP and custom connectors. Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector),hybrid retrieval with ElasticSearch or Solr, and BM25 + similarity search. Work across relational, document, key-value, and graph stores ...

Competence in containerized deployments, CI/CD, and MLOps tooling (MLFlow, Airflow)Experience with Microsoft Agent Framework, Azure AI Agent ServiceKnowledge of vector databases (Pinecone, Weaviate, Qdrant, Milvus)Familiarity with guardrail and AI safety techniques (output filtering, prompt injection defense)Experience in distributed systems, event-driven architectures, and workflow enginesPrior ...

with: Agentic frameworks: LangGraph, LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector Cloud platforms: AWS Bedrock, GCP Vertex AI or Azure AI Studio CI/CD for AI: version-controlled prompts, automated testing ...

multi-agent workflows and autonomous AI systems Knowledge of the Model Context Protocol (MCP) and AI integration patterns Experience with vector databases such as Pinecone, Weaviate, FAISS, or Azure AI Search Experience working with OpenAI and other leading foundation models Knowledge of real-time AI pipelines, including speech-to-text ...

methodologies and version control systems like Git. Exposure to cloud platforms (AWS, Azure, or Google Cloud). Basic awareness of Vector Databases (e.g., pgvector, Pinecone) or RAG (Retrieval-Augmented Generation) concepts. Any foundational Blue Prism certifications. Why You Will Love It Here! Flexibility: Hybrid Work ModelYour Future: Professional Development Reimbursement ...

etc.) critically and effectively as part of their day-to-day workflow.Bonus points for:Experience with retrieval-augmented generation (RAG) and vector databases (pgvector, Pinecone, or similar).Experience designing multi-agent systems and agent orchestration patterns.Familiarity with LLM observability tools (LangSmith, Langfuse, Arize, or similar).Experience working with multiple model ...

securely, and consistently across teams.• Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding … Semantic Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI/AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.• Architecture Design: Ability to design end-to-end Generative and Agentic ...

practices, ideally in a regulated environment. Preferred qualifications:Background in financial services or another regulated enterprise environment. Experience with vector databases (for example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such ...