22 of 22 Pinecone Jobs in the UK

Learn LangChain LangGraph Hugging Face FastAPI REST APIs Data Technologies SQL NoSQL Databases Data Modelling ETL Development Data Pipeline Engineering Vector Databases such as Pinecone, Weaviate or ChromaDB Cloud & DevOps Experience with at least one major cloud platform: AWS (preferred) Bedrock SageMaker Lambda ECS/EKS S3 Or experience within ...

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 ...

LlamaIndex, and Hugging Face, including agentic and multi‐step workflows* **Proven experience designing and implementing Retrieval‐Augmented Generation (RAG) systems**, including vector databases (pgvector, Pinecone, Weaviate, FAISS, etc.) and advanced retrieval strategies (hybrid search, re‐ranking, metadata filtering)* **Strong understanding of prompt engineering, workflow design, and Gen‐AI optimization**, balancing ...

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 ...

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 ...

leveraging AI/GAI technologies and large language models (LLMs) Advanced AI Integration : Apply experience with retrieval-augmented generation (RAG), vector databases (e.g. Pinecone, Weaviate, FAISS), and enterprise search for AI-driven knowledge discovery Optimize AI Performance : Utilize practical experience designing structured prompts, fine-tuning models, and cost optimization strategies ...

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 ...

systems, not just prototypesDemonstrable experience of building and deploying complex AI applications using LLMs and ideally, agentic workflowsA familiarity with vector databases (pgvector, ChromaDB, Pinecone or similar) is a plusBenefits include:Flexibility - the team works fully remotely, albeit with frequent meetups to share ideasStock optionsExcellent opportunities for progression and development ...

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 ...

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 ...

architectures (graph-augmented retrieval for grounded generation) – Semantic layer and business intelligence platforms (Looker, dbt Semantic Layer, AtScale) – Vector databases and hybrid retrieval (Qdrant, Pinecone, pgvector, AlloyDB vector search) – Cloud data platforms at scale (BigQuery, Snowflake, Databricks, Spanner) – Data cataloging and governance (Google Knowledge Catalog/Dataplex, Collibra, Alation, Atlan ...

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 ...