18 of 18 Permanent Pinecone Jobs in the UK

pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely with More ❯

pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely with More ❯

APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow More ❯

APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow More ❯

APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow More ❯

APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow More ❯

and conversational AI (e.g., GPT, Claude, Mistral, LLaMA, etc.) Expertise with frameworks like LangChain, Hugging Face, OpenAI, RAG pipelines, and vector databases (e.g., Weaviate, Pinecone, Chroma) Solid knowledge of AI system architecture, including model serving, monitoring, and optimization Strong programming skills in Python, with experience building APIs and integrating backend More ❯

continuous integration and deployment tools such as GitLab , GitHub , or Jenkins . Database Management Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Version Control Proficient in More ❯

Deployment: Strong hands-on experience developing and deploying AI/ML models. RAG Expertise: Experience in Retrieval-Augmented Generation and related vector databases (e.g., Pinecone, FAISS, Weaviate). LLMs & NLP: Experience working with LLMs (OpenAI, Anthropic, Hugging Face, etc.), including model tuning, security, and optimization. Regulatory Knowledge: Understanding of AI More ❯

document-based GenAI solutions, including RAG pipelines, prompt engineering, and integration with internal APIs. Help orchestrate tools using OpenAI function calling, vector databases (e.g. Pinecone, Weaviate), and knowledge graphs. Collaborate on the deployment of AI agents as secure microservices using Azure Kubernetes Services (AKS) and monitor system performance. Draft structured More ❯

SageMaker, Bedrock, Lambda, S3, ECS, EKS Full-Stack Integration: Build APIs (FastAPI, Flask) and integrate with React, TypeScript, Node.js Vector Search: Use FAISS, Weaviate, Pinecone, ChromaDB, OpenSearch Required skills & experience: 3–5+ years of experience in ML engineering and software development Deep Python proficiency, with PyTorch, TensorFlow or Hugging Face More ❯

and improving developer workflows. Excellent communication and collaboration skills in a remote-first environment. Experience contributing to open-source AI projects. Experience with LangChain, Pinecone, or similar AI frameworks/infrastructure. Past experience building AI features into developer platforms or tools. Benefits Our entire company is distributed, so we take More ❯

Develop and optimise RAG pipelines using LangChain, LlamaIndex, or Haystack. Build ingestion workflows (OCR, chunking, embedding, semantic search) and integrate with vector databases (FAISS, Pinecone, Qdrant). Ensure seamless integration of GenAI services into business workflows, prioritising security, scalability, and compliance. Collaborate with cross-functional teams (data scientists, architects, engineers More ❯

to work autonomously, listen to suggestions, make recommendations, focus on delivery. Bonus Points For Experience with Next.js, API data ingestion, or vector databases (e.g., Pinecone, Weaviate, pgvector, qdrant). Exposure to embedding models, LLMs, or hybrid retrieval/generation architectures. Previous projects (personal, academic, or open source) related to AI More ❯

Experience extending an *AWS stack (Terraform, ECS Fargate, ALB, Secrets Manager, KMS)*. •⁠ ⁠Hands-on with *LLM APIs* and at least one *vector database* (Pinecone, Weaviate, OpenSearch, etc.). •⁠ ⁠Multi-tenant data design with GDPR awareness. •⁠ ⁠CI/CD and automated-testing mindset. Nice-to-Haves •⁠ ⁠*Solution-architect background*—ability More ❯

others for prompt and model optimisations. Comfortable working with databases (relational & vector), and large-scale data sets and pipelines (e.g. AWS Glue, Redshift, RDS, Pinecone, Opensearch). Hands-on experience with AI Cloud Infrastructure for MLOps (e.g. Google Vertex AI/AWS Bedrock), including deploying AI applications and managing AI More ❯

others for prompt and model optimisations. Comfortable working with databases (relational & vector), and large-scale data sets and pipelines (e.g. AWS Glue, Redshift, RDS, Pinecone, Opensearch). Hands-on experience with AI Cloud Infrastructure for MLOps (e.g.Google Vertex AI/AWS Bedrock), including deploying AI applications and managing AI cloud More ❯

retrieval strategies using vector/graph database technologies. Experience in developing ingestion workflows/pipelines for vector indexes using well-known providers (e.g., FAISS, Pinecone, Weaviate, Chroma). Experience with testing methodologies (unit/functional/e2e) and tools (unittest, pytest, etc.). Experience with agile development methodologies and version … APIs and experience in API design and implementation. Experience in developing ingestion workflows/pipelines for vector indexes using well-known providers (e.g., FAISS, Pinecone, Weaviate, Chroma). Build and deploy robust AI applications using technologies such as Llamanindex and LangChain for seamless orchestration of LLM (large language model) pipelines. More ❯