21 of 21 Pinecone Jobs in the UK

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 ...

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 ...

regularisation, statistics Production ML experience—deploying, serving and monitoring models at scale LLM and generative AI tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation ...

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 ...

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 ...

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 ...

Microservices Architecture Cloud platforms - AWS, Azure, or GCP Preferred Agentic framework experience - LangChain, LangGraph, AutoGen, or similar RAG pipeline design and vector database experience (Pinecone, Weaviate, pgvector) Agile/SDLC tooling - Jira, Git, CI/CD pipelines At BNY, our culture allows us to run our company better and enables ...

Azure), CI/CD pipelines, and scalable service architecture. Data and Retrieval Systems Experience building and operating knowledge layers, vector databases (Pinecone, Weaviate, pgvector), document ingestion pipelines, embedding workflows, search and retrieval optimization. Nice to Have MCP architectures or tool‐connected AI systems, agent frameworks, knowledge graph systems, streaming ...

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 ...

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 ...

Architect agent loops: planner/executor/verifier patterns, tool‐use schemas, retry + escalation logic. Build retrieval pipelines: chunking strategy, vector DB choice (Pinecone/pgvector/Weaviate), reranking, freshness handling. Stay current on Anthropic + OpenAI release notes; adjust prompts + schemas when APIs evolve. We treat ...

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 ...

Pragmatic mindset with a strong sense of ownership Excellent communication and collaboration skills Nice-to-have MongoDB and OpenSearch Vector databases (Qdrant, Weaviate, Milvus, Pinecone, pgvector) OIDC or identity infrastructure Infisical, PostHog, Oh Dear, or similar tooling AI/LLM platform experience GDPR-conscious cloud environments Enterprise security and compliance ...

just prototypes Demonstrable experience of building and deploying complex AI applications using LLMs and ideally, agentic workflows A familiarity with vector databases (pgvector, ChromaDB, Pinecone or similar) is a plus Benefits include: Flexibility - the team works fully remotely, albeit with frequent meetups to share ideas Stock options Excellent opportunities ...

just prototypes Demonstrable experience of building and deploying complex AI applications using LLMs and ideally, agentic workflows A familiarity with vector databases (pgvector, ChromaDB, Pinecone or similar) is a plus Benefits include: Flexibility - the team works fully remotely, albeit with frequent meetups to share ideas Stock options Excellent opportunities ...

just prototypes Demonstrable experience of building and deploying complex AI applications using LLMs and ideally, agentic workflows A familiarity with vector databases (pgvector, ChromaDB, Pinecone or similar) is a plus Benefits include: Flexibility - the team works fully remotely, albeit with frequent meetups to share ideas Stock options Excellent opportunities ...

just prototypes Demonstrable experience of building and deploying complex AI applications using LLMs and ideally, agentic workflows A familiarity with vector databases (pgvector, ChromaDB, Pinecone or similar) is a plus Benefits include: Flexibility - the team works fully remotely, albeit with frequent meetups to share ideas Stock options Excellent opportunities ...

just prototypes Demonstrable experience of building and deploying complex AI applications using LLMs and ideally, agentic workflows A familiarity with vector databases (pgvector, ChromaDB, Pinecone or similar) is a plus Benefits include: Flexibility - the team works fully remotely, albeit with frequent meetups to share ideas Stock options Excellent opportunities ...

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 ...