15 of 15 Pinecone Jobs in London

Azure, AWS or GCP, alongside CI/CD, Docker, Kubernetes and Infrastructure as Code. Experience with vector databases and search technologies such as Pinecone, Weaviate or Azure AI Search. Strong understanding of AI governance, security, privacy and responsible AI principles. Comfortable working in client-facing environments and communicating effectively with ...

Supervised/unsupervised ML and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases (Pinecone, Weaviate, FAISS). Experience in designing and scaling GenAI chatbots. Experience in Agentic AI solutions. Good knowledge of Agentic AI frameworks (LangGraph, AutoGen, CrewAI ...

Experience with continuous integration and deployment tools such as GitLab, GitHub, or Jenkins. 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. Proficient in Git and version ...

Experience with continuous integration and deployment tools such as GitLab, GitHub, or Jenkins. 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. Proficient in Git and version ...

Claude Code, Cursor, GitHub Copilot) to accelerate software solution development in production environments. Hands-on experience with modern AI infrastructure, including vector databases (e.g., Pinecone, Weaviate, Qdrant), cloud platforms (Azure, AWS, or GCP), API design, and containerized deployment (Docker, Kubernetes). Experience designing data pipelines and document processing systems ...

help you with them: Experience with advanced LLM techniques: agents, tool use, and agentic workflows Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications Experience with feature stores (Feast, AWS Feature Store) Experience with containerisation (Docker) and orchestration (Kubernetes, ECS) Familiarity with Infrastructure as Code (Terraform, CloudFormation) Experience with ...

these, but if not well help you with them: Experience with advanced LLM techniques: agents, tool use, and agentic workflows Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications Experience with feature stores (Feast, AWS Feature Store) Experience with containerisation (Docker) and orchestration (Kubernetes, ECS) Familiarity with Infrastructure ...

help you with them: Experience with advanced LLM techniques: agents, tool use, and agentic workflows Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications Experience with feature stores (Feast, AWS Feature Store) Experience with containerisation (Docker) and orchestration (Kubernetes, ECS) Familiarity with Infrastructure as Code (Terraform, CloudFormation) Experience with ...

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 ...

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 ...

specificHands on experience with LLM APIs or open source models such as Llama or MistralExperience building RAG systems with vector databases such as FAISS, Pinecone or WeaviateAbility to evaluate and improve prompts and retrieval quality using clear metricsUnderstanding of safety practices such as PII redaction and content filteringExposure to agentic ...

matrixed organizations. Preferred QualificationsFamiliarity with AI ecosystems such as LangGraph, Semantic Kernel, OpenAI, Azure OpenAI, or Anthropic, and/or vector stores such as Pinecone, Weaviate, OpenSearch, or PostgreSQL pgvector. Experience building AI-powered applications in regulated industries, including financial services or trading environments. Knowledge of enterprise architecture frameworks, governance ...

LangChain (FrameWork), LangGraph, Linguaggio di programmazione Python, LlamaIndex, Microsoft Azure, MLflow, Modelli linguistici di grandi dimensioni (LLM), Operazioni di machine learning, OWASP Top 10, Pinecone (database vettoriale), Progettazione della sicurezza, Programmazione Informatica, Sicurezza dei dati, Sicurezza dei sistemi operativi {+ 6 more} Why GSK? Uniting science, technology and talent ...

using orchestration libraries like LangChain, CrewAI, or StackAI. Deep programmatic fluency with LLM APIs (specifically Anthropic Claude and OpenAI SDKs), vector databases (such as Pinecone or Chroma), and retrieval-augmented generation concepts. Hands-on experience developing with Model Context Protocol (MCP) Strong software development fundamentals in Python and JavaScript/ ...

agents across client use cases Work across LangGraph, LangChain, Claude Code/Codex/Gemini and RAG pipelines Implement RAG and vector database solutions (Pinecone, Weaviate, LlamaIndex) Deploy across cloud platforms: AWS Bedrock, GCP Vertex AI or Azure AI Studio Bridge CX and engineering Hold the full stack from … 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 Leadership & consulting skills Clear communication; able to explain technical decisions ...