26 to 50 of 56 Pinecone Jobs in the UK

approach to the software development lifecycle (SDLC). Desirable: AI & Data Underpinnings Vector Databases: Experience with high-dimensional data retrieval using Milvus, Weaviate, or Pinecone (essential for RAG architectures). Data Orchestration: Familiarity with Airflow, Prefect, or Dagster for managing complex data movement and model training pipelines. Search & Discovery: Advanced ...

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 ...

organizations. **Preferred Qualifications*** Familiarity 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 ...

organizations. Preferred Qualifications Familiarity 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 ...

locations, green values) with highly relevant sustainable vendor profiles and local circular economies. Infrastructure & Data Pipelines: Build and optimize scalable vector database infrastructures (e.g., Pinecone, Milvus, or pgvector) and real-time streaming architectures alongside backend tech stacks. Technical Leadership & Governance: Pioneer technical guardrails around AI model evaluation, data bias minimization ...

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 ...

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 ...

e.g. NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, Braintrust)MLOps & LLMOps• Hands-on with MLOps platforms (Azure ML, Databricks, SageMaker) and vector/retrieval databases (Pinecone, Milvus, pgvector)• Experience with GPU-accelerated infrastructure and NVIDIA AI Enterprise or equivalent stacks• Exposure to fine-tuning, RLHF, or SLM distillation is a strong ...

e.g. NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, Braintrust) MLOps & LLMOps • Hands-on with MLOps platforms (Azure ML, Databricks, SageMaker) and vector/retrieval databases (Pinecone, Milvus, pgvector) • Experience with GPU-accelerated infrastructure and NVIDIA AI Enterprise or equivalent stacks • Exposure to fine-tuning, RLHF, or SLM distillation is a strong ...

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 ...

operations. Experience building or supporting RAG (Retrieval-Augmented Generation) architectures. Strong understanding of LLMs and production GenAI workloads. Vector database/search experience with Pinecone, Weaviate, pgvector or Azure AI Search . Experience with LangChain, LlamaIndex and/or MCP (Model Context Protocol) . LLM observability/evaluation experience using ...

models like Llama/Mistral) and orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent). Practical expertise in RAG design, embeddings, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search). Experience with agentic architectures, function/tool calling, and structured outputs. Solid prompt engineering, evaluation, and LLM guardrail/ ...

inference-cost optimisation Scalable cloud-based AI deployment Experience with technologies such as LangGraph, LangChain, LlamaIndex, AutoGen, Google Vertex AI, Gemini, BigQuery, Qdrant, ChromaDB, Pinecone, Weaviate or Milvus would be valuable. Network Programmability Hands-on network programmability experience is not essential , but you should understand the technologies and tools likely ...

inference-cost optimisation Scalable cloud-based AI deployment Experience with technologies such as LangGraph, LangChain, LlamaIndex, AutoGen, Google Vertex AI, Gemini, BigQuery, Qdrant, ChromaDB, Pinecone, Weaviate or Milvus would be valuable. Network Programmability Hands-on network programmability experience is not essential , but you should understand the technologies and tools likely ...

your own) and a clear view on when agents are the right tool versus when they’re overkill Comfortable working with vector stores (Pinecone, Weaviate, Chroma, pgVector, or similar) Solid software engineering fundamentals — testing, CI/CD, observability — applied to non-deterministic systems Comfortable in client‐facing settings, including running ...

treat developers as customers and measure whether they adopt what you build. Desirable Experience: Experience with RAG architectures and vector databases (for example Pinecone, Weaviate, pgvector, Azure AI Search). Familiarity with agent and orchestration frameworks such as LangChain, LlamaIndex or the Model Context Protocol (MCP). LLM observability ...

software engineering, solutions engineering, or ML deployment role Proficiency in Python and working knowledge of AI/ML frameworks (e.g., OpenAI, Hugging Face, LangChain, Pinecone, Weaviate) Familiarity with building LLM applications, RAG systems, or conversational AI workflows Experience integrating with enterprise systems via APIs or middleware (e.g., Salesforce, ServiceNow ...

production as APIs or services, with attention to latency, cost, scalability, and reliability Design and implement RAG (Retrieval‐Augmented Generation) systems using vector databases (Pinecone, Weaviate, Chroma), including embedding, chunking, and retrieval‐quality evaluation Integrate LLM APIs (OpenAI, Anthropic, etc.) into production services and product workflows Design and build agentic … OpenAI, Anthropic; LangChain or LlamaIndex Agentic AI – LangGraph, LangChain Agents, AutoGen, or CrewAI; tool/function calling, orchestration, memory, and guardrails RAG & vector databases —-Pinecone, Weaviate, or Chroma Databases – PostgreSQL, MongoDB, or Firebase Cloud & deployment – AWS, GCP, or Azure; Docker; CI/CD Security & compliance – secure coding, secrets management, access ...

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. ...

operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent). Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector). Practical experience implementing RAG architectures or LLM-integrated data pipelines. Familiarity with semantic web standards—JSON-LD, RDF, OWL, or SKOS. Strong ...

operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent). Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector). Practical experience implementing RAG architectures or LLM-integrated data pipelines. Familiarity with semantic web standards—JSON-LD, RDF, OWL, or SKOS. Strong ...

working with AI tools and prompting. An understanding of point in time data is a strong plus Also desirable: experience with vector databases (pgvector, Pinecone, Weaviate, Qdrant or similar); machine learning experience (PyTorch, TensorFlow, XGBoost/LightGBM) for building, maintaining and extending existing models; Bloomberg API integration to Python; data ...

ontology and semantic technologies: Neo4j, Amazon Neptune, Stardog, GraphDB, RDF, OWL, SPARQL AI/ML data infrastructure: vector databases and search platforms such as Pinecone, Weaviate, Milvus, Azure AI Search, OpenSearch or pgvector; feature stores such as Feast or Tecton; model lifecycle and experiment tracking tools such as MLflow Personal ...

frameworks like PyTorch or TensorFlow . Hands-on experience with large language models, prompt engineering, and RAG pipelines . Familiarity with vector databases (Pinecone, Weaviate, Qdrant, or similar). Solid understanding of SQL and relational database concepts. Experience building evaluation and testing frameworks for AI systems. We invest ...

Qualifications Experience with Digital Twin platforms Experience with Knowledge Graph implementations Data Mesh architectures LangChain, LangGraph and LlamaIndex Vertex AI and Gemini Vector databases (Pinecone, Weaviate, Milvus, Qdrant) Kubernetes and GKE Autonomous Network and AI-Ops programmes If you're excited about this role but don’t meet every requirement ...