1 to 25 of 27 Permanent 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 ...

generation Work with OpenAI, Anthropic, Mistral, Llama and similar LLMs Use LangChain, LangGraph, CrewAI or similar AI frameworks Work with vector databases including Pinecone, Qdrant, ChromaDB and PGVector Take GenAI POCs into production and scale them Build APIs, microservices and AI integrations Deploy solutions using AWS/Azure, Docker ...

generation Work with OpenAI, Anthropic, Mistral, Llama and similar LLMs Use LangChain, LangGraph, CrewAI or similar AI frameworks Work with vector databases including Pinecone, Qdrant, ChromaDB and PGVector Take GenAI POCs into production and scale them Build APIs, microservices and AI integrations Deploy solutions using AWS/Azure, Docker ...

generation Work with OpenAI, Anthropic, Mistral, Llama and similar LLMs Use LangChain, LangGraph, CrewAI or similar AI framework Work with vector databases including Pinecone, Qdrant, ChromaDB and PGVector Take GenAI POCs into production and scale them Build APIs, microservices and AI integrations Deploy solutions using AWS/Azure, Docker ...

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 ...

using Azure AI Foundry or Microsoft Agent FrameworkKnowledge of MCP and A2A protocols for agent and tool interoperabilityHands-on work with vector databases like Pinecone, Weaviate, Qdrant or pgvectorFamiliarity with distributed systems, workflow engines (Temporal, Airflow or Dagster) and event-driven architecturesExperience with open-source LLMs or Small Language Models ...

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 ...

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

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 ...

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 ...

degree in Computer Science, Data Science, or related fieldNice to haveExperience with Azure AI Agent Service or Microsoft Agent FrameworkFamiliarity with vector databases (Pinecone, Weaviate, Qdrant, Milvus)Knowledge of AI safety techniques such as guardrails, bias mitigation, and model explainabilityUnderstanding of Intelligent Document Processing, data lineage, and distributed memory ...

containerized deployments, CI/CD, and MLOps tooling (MLFlow, Airflow)Nice to haveExperience 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 ...

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 ...

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 ...

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 ...

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 ...

daily communication with the team. Preferred RequirementsExperience with legal technology or document-heavy domains. Familiarity with vector databases and semantic search (e.g. Elasticsearch, OpenSearch, Pinecone, FAISS).Knowledge of prompt engineering, fine-tuning, and evaluation techniques for LLMs. Experience integrating AI safety, compliance, and explainability into deployed solutions. Exposure to front ...

EngineeringWork with AI+ML pipelines through Azure ML, AWS SageMaker, Vertex AI, Databricks, or Modal/Fly.io for lightweight LLM deployment. Utilize vector databases (Pinecone, Weaviate, Milvus, ChromaDB, pgVector) and embedding stores. Use AI-powered dev tools (GitHub Copilot, Cursor, Codeium, Aider, Windsurf) to accelerate iteration. Implement LLMOps/PromptOps using … LlamaIndex and the broader GenAI ecosystem. Deep understanding of LLM behavior, prompt optimization, embeddings, retrieval and data preparation workflows. Experience with vector DBs (FAISS, Pinecone, Milvus, Weaviate, ChromaDB).Hands-on knowledge of Linux, Bash/Powershell, containers and cloud environments. Strong communication skills, creativity and a systems-thinking mindset. Curiosity ...

similar. Experience working with Generative AI and ML infrastructure, including: Fine-tuning transformer models Managing embeddings Building RAG pipelines Familiarity with vector databases (e.g., Pinecone, Milvus, Weaviate) and semantic search techniques. Strong foundation in cloud infrastructure, networking, security, CI/CD pipelines, and observability. Experience with cloud platforms ...

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