14 of 14 Permanent Pinecone Jobs in London

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 ...

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 ...

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 ...

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 ...

graphs for context enrichment, reasoning, and workflow automation RAG & Knowledge Systems: End‐to‐end RAG pipelines using enterprise search + vector stores (e.g., Elastic, Pinecone) with grounding, evaluation, and optimization Model Lifecycle & Governance: Model evaluation, monitoring, prompt/version control, and Responsible AI/MRM compliance Enterprise AI Integration & Data ...

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 ...

Computer Science, Data Science, or related field Nice to have Experience with Azure AI Agent Service or Microsoft Agent Framework Familiarity with vector databases (Pinecone, Weaviate, Qdrant, Milvus) Knowledge of AI safety techniques such as guardrails, bias mitigation, and model explainability Understanding of Intelligent Document Processing, data lineage, and distributed ...

with LLMs and Generative AI frameworks.* Hands-on experience with:* LangChain/LangGraph* Semantic Kernel* OpenAI APIs/Azure OpenAI* Hugging Face* Vector Databases (Pinecone, FAISS, ChromaDB, Weaviate, Milvus)* Machine Learning fundamentals.* NLP concepts and transformer architectures.* Model evaluation and observability.* Data preprocessing and feature engineering.## **We are a Disability ...

skills. Preferred Skills Experience with LangChain, LangGraph, Semantic Kernel, CrewAI or similar agent frameworks. Experience with Bedrock Knowledge Bases, vector databases such as OpenSearch, Pinecone or Weaviate, and embedding-based retrieval patterns. Experience with MCP (Model Context Protocol), enterprise tool integration and workflow automation. Knowledge of AI safety, guardrails, governance ...

tasks reliably and safely. Retrieval‐Augmented Generation (RAG) & Knowledge Systems : Proven experience designing RAG and knowledge‐grounded systems, including chunking strategies, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and evaluation of retrieval quality. Familiarity with advanced patterns such as GraphRAG and agentic RAG to reduce hallucination … driven and orchestration patterns Cloud AI platforms & model hosting: Amazon Bedrock, Azure OpenAI/AI Foundry, Google Vertex AI, Hugging Face Vector databases & retrieval: Pinecone, Weaviate, Chroma, pgvector, FAISS; embeddings, semantic and hybrid search, reranking MLOps/LLMOps & deployment: Docker, Kubernetes, FastAPI, CI/CD; observability, tracing, evaluation tooling (LangSmith ...

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 ...

frameworks and autonomous agent architectures. Proficiency in Python and modern software development practices (testing, code review, CI/CD). 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 ...

token usage across multiple LLM providers (Anthropic, OpenAI, open-source models via vLLM) Own database performance for both relational (Postgres) and vector (pgvector/Pinecone) workloads Collaborate with ML engineers on model-serving infrastructure and prompt-caching strategies Drive API versioning and backwards compatibility as the product iterates quickly Core … Tech Stack: Languages: Python (FastAPI), Go, TypeScript (Node.js) Databases: PostgreSQL, Redis, Pinecone/pgvector Infra: AWS (ECS, Lambda, SQS/SNS), Docker, Kubernetes, Terraform AI/ML tooling: LangChain/LlamaIndex, vLLM, Anthropic & OpenAI APIs, embedding models Observability: Datadog, Grafana, OpenTelemetry CI/CD: GitHub Actions, ArgoCD Requirements: 4+ years ...