23 of 23 Pinecone Jobs in the UK

LLMs) from OpenAI, Anthropic, Google Gemini, and Mistral, including deployment through Azure OpenAI Service or Vertex AI. Implement RAG architectures and manage vector databases (Pinecone, Weaviate, FAISS, Milvus) to power enterprise knowledge intelligence platforms. Data Platform & Engineering Excellence Lead the evolution of the enterprise data landscape using modern platforms such ...

frameworks and orchestration AI/ML Engineering Python, PyTorch, TensorFlow, JAX NLP, deep learning, model optimisation Evaluation frameworks and guardrails Data & Platforms Vector databases (Pinecone, Weaviate, FAISS, Milvus) Knowledge graphs and semantic search Data engineering pipelines MLOps & Deployment CI/CD for ML, model monitoring, versioning Kubernetes, Docker, serverless architectures ...

/ML platforms such as Azure ML, AWS SageMaker, Vertex AI, Databricks, Modal, and Fly.io. Deploy and manage vector databases and embedding stores, including Pinecone, Weaviate, Milvus, FAISS, ChromaDB, and pgVector. Implement LLMOps/PromptOps practices using tools such as: Weights & Biases, MLflow, LangSmith, LangFuse, PromptLayer, Humanloop, Helicone, Arize Phoenix ...

MLOps/LLMOps pipelines, and AI observability. Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases (Pinecone, Weaviate, FAISS, etc.). Ability to design enterprise-level AI/Gen AI platform/solutions with the client's existing enterprise stack. Hands ...

production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within enterprise environments. Strong grasp of advanced techniques such as complex ...

production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within enterprise environments. Strong grasp of advanced techniques such as complex ...

services → application integrations ; ensure scalability, security, and observability. Select and integrate LLMs (Azure OpenAI, Google, AWS) and design RAG pipelines with vector stores (e.g., Pinecone , FAISS , Azure Cognitive Search ) and prompt strategies; optimize for latency/cost/accuracy. Architect agentic AI components (multi-agent workflows, gateways, registries, protocol interoperability ...

such as GPT, Claude, Gemini as well as self-hosted LLMs along with MCP integrations Implement Retrieval-Augmented Generation (RAG) techniques leveraging vector databases (Pinecone/ChromaDB/Weaviate/pgvector/etc), search engines such as ElasticSearch/Solr using both TF/IDF BM25 based full text search ...

unsupervised) MLOps/LLMOps and AI observability Hands-on experience building enterprise RAG solutions using LLMs (e.g. OpenAI, Llama, Mistral, Claude) and vector databases (Pinecone, Weaviate, FAISS, etc.) Practical experience with GenAI frameworks such as LangChain, LlamaIndex, and agentic frameworks like AutoGen, CrewAI, LangGraph Strong Python expertise with AI/ ...

integration Strong knowledge of Python, MLOps, LLMOps, CI/CD, model monitoring Experience delivering enterprise AI solutions at scale Desirable Experience with vector databases (Pinecone, FAISS) AI governance, compliance and security architecture Azure AI/AWS ML/Kubernetes certifications This role suits a technically hands-on architect ...

pipelines, and model monitoring practices Proven track record of delivering large-scale enterprise AI platforms Desirable Skills Experience working with vector databases such as Pinecone or FAISS Knowledge of AI governance frameworks, compliance standards, and security architecture Relevant certifications in Azure AI, AWS ML, or Kubernetes This opportunity is well ...

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 ...

Create front-end applications with React, TypeScript, and frameworks like Next.js or Vite. Integrate LLMs (e.g., OpenAI, Anthropic, Mistral) and vector databases (e.g., ChromaDB, Pinecone, PGVector). Deploy solutions using AWS or Azure, Docker, Kubernetes, and Terraform. Implement CI/CD pipelines and monitor LLM performance using tools like Langsmith ...

LangChain, CrewAI) in production. Strong Python engineering skills with solid testing, optimisation, CI/CD, and code quality; Experience implementing and scaling vector databases (Pinecone, Weaviate, Milvus) for retrieval and reasoning workloads. Deep expertise in cloud‐native AI engineering on AWS, with Azure knowledge and ability to design distributed, fault ...

workflows Designing and maintaining backend services and APIs (primarily Python, FastAPI/Flask) Building and scaling data pipelines and search systems using: ElasticSearch, Weaviate, Pinecone, and other vector databases Engineering secure, containerised architectures using Docker and Kubernetes Ensuring high availability, fault tolerance, and performance for mission-critical workloads Providing technical ...

Bring Technical Excellence: Deep expertise in Python and frameworks like LangChain , LlamaIndex , or Haystack . Vector Expertise: Proven experience with vector databases such as Pinecone, Weaviate, or Milvus . Cloud Architecture: Strong proficiency in deploying AI solutions within AWS or Azure ecosystems. Industry Context: (Bonus) A background in FinTech ...

production Strong Python development skills Hands-on experience with: Prompt engineering and evaluation Retrieval-Augmented Generation (RAG) Embeddings and vector databases (e.g. FAISS, Pinecone, Weaviate, Chroma) Experience building LLM-backed services using FastAPI/Flask or similar Strong understanding of trade-offs around accuracy, latency, cost and scalability Highly Desirable ...

required What We're Looking For Essential – Strong Python — production-quality services, not just scripts – Data pipelines and ELT workflows in production – Vector databases (Pinecone, Weaviate, Qdrant or similar) and semantic/hybrid search – LLM API integration in live systems – Docker and containerisation; familiarity with orchestration – API design, security ...

review. AI/ML Skills: LLMs & NLP: Experience with OpenAI API, LangChain, or HuggingFace Transformers for text-to-field understanding. Vector Databases: Proficiency in Pinecone, Milvus, or Weaviate for managing high-dimensional data embeddings. Automation: Building "Human-in-the-Loop" (HITL) workflows to retrain models based on SME feedback. Tools ...

your instinct is to build the solution, not write a process document about it. Nice to Haves Experience with vector databases (e.g., pgvector, Pinecone) or building RAG pipelines. Familiarity with recruitment CRMs (JobAdder, Bullhorn, Vincere, etc.). Experience deploying services on Railway, Render, or similar PaaS platforms. Working knowledge ...

Stack: Deep expertise in Google Cloud Platform (GCP) , Vertex AI , and Gemini Models . AI Frameworks: Strong hands-on experience with LangChain , Vector Databases (Pinecone, Weaviate, etc.), and LLMOps tools. Domain: Experience delivering AI/GenAI use cases within Banking or highly regulated industries is a significant plu Why Apply ...

build systems that scale Experience building and shipping AI agents and LLM-powered systems in production Strong RAG experience; with hands on vector databases (Pinecone, pgvector or similiar) They are based in London with their engineering team operating either hybrid or remote. ⚡ This is a fantastic opportunity sitting right ...

generating very large volumes of time-series data. Hands-on understanding of stream ingestion patterns (MQTT). Experience with graph or Vector databases ( Neo4j, Pinecone, pgvector) and modelling complex, highly relational domains. Familiarity with modern data stack components (e.g., data lakes, streaming, or batch/real-time pipelines). ...