1 to 25 of 47 Pinecone Jobs in London

custom LLM integrations). Exposure to AI ethics, data privacy, and compliance regulations. Prior experience in multi-agent systems or autonomous AI workflows. Hands-on experience with vector databases (Pinecone, Weaviate, FAISS) and AI embeddings. Remote WorkingSome remote working CountryUnited Kingdom LocationWC1 Job TypeContract or Permanent Start DateApr-Jul 25 Duration9 months initial or permanent Visa RequirementApplicants must be eligible More ❯

/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers , or TensorFlow. Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex). Hands-on experience with fine-tuning and distillation of large language models. Comfortable with cloud platforms (Azure preferred), CI/CD tools More ❯

/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers, or TensorFlow. Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex). Hands-on experience with fine-tuning and distillation of large language models. Comfortable with cloud platforms (Azure preferred), CI/CD tools More ❯

RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis, Azure AI Search) to support semantic search and recommendation systems. Building intelligent features like AI-powered chatbots , assistants , and question-answering systems using LLMs and conversational agents. Awareness of More ❯

RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis, Azure AI Search) to support semantic search and recommendation systems. Building intelligent features like AI-powered chatbots , assistants , and question-answering systems using LLMs and conversational agents. Awareness of More ❯

RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis, Azure AI Search) to support semantic search and recommendation systems. Building intelligent features like AI-powered chatbots , assistants , and question-answering systems using LLMs and conversational agents. Awareness of More ❯

in Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenisation, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating LLM-powered systems such as chatbots, summarisation tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding More ❯

prompt engineering, and deployment Python Proficiency : Strong skills with ML frameworks (TensorFlow, PyTorch) and LLM tools (Hugging Face, LangChain, OpenAI APIs) Vector Understanding : Solid knowledge of embeddings, vector databases (Pinecone, Weaviate, FAISS), and RAG pipelines NLP Fundamentals : Text preprocessing, language modelling, and semantic similarity Cloud Experience : AWS ecosystem knowledge (SageMaker, Lambda, etc.) Production Ready : ETL pipelines, version control, Agile methodologies More ❯

in Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating LLM-powered systems such as chatbots, summarization tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding More ❯

in Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating LLM-powered systems such as chatbots, summarization tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding More ❯

flows, compliance, user segmentation, etc.) Technical Skills: Proficient in Python, SQL, and data science libraries (Pandas, NumPy, Scikit-learn, Hugging Face Transformers) Familiarity with embedding models, vector databases (e.g., Pinecone, FAISS, Weaviate) Experience with cloud platforms (AWS, GCP, or Azure) and MLOps pipelines Solid understanding of NLP, LLM fine-tuning, and prompt engineering Preferred Qualifications Familiarity with customer analytics and More ❯

What you’ll do Design & build backend micro‐services (Python/FastAPI) that power RAG pipelines, user queries, and analytics. Develop retrieval infrastructure : orchestrate embedding generation, vector databases (PGVector, Pinecone, Weaviate), and hybrid search. Implement evaluation framework for search quality and answer accuracy (BLEU/ROUGE, human‐in‐the‐loop, automatic hallucination checks). Deploy & monitor services on GCP (Cloud … ship weekly increments. Champion best practices in testing, secure data handling (NHS DSPT), and GDPR compliance. Tech you’ll use Python • FastAPI • LangChain/LlamaIndex • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • Prometheus/Grafana • GitHub Actions What we’re looking for Master’s degree in Computer Science, Software Engineering, or related field; or More ❯

optimize RAG pipelines using frameworks such as LangChain, LlamaIndex, or Haystack. Build data ingestion workflows including OCR, chunking, embedding, and semantic search integration. Integrate vector databases such as FAISS, Pinecone, or Qdrant into AI workflows. Deliver scalable GenAI services aligned with security, compliance, and enterprise standards. Collaborate with data scientists, architects, and engineers to implement high-performance AI solutions. Proven More ❯

monitoring. Full-Stack Integration : Develop 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 or Hugging Face Transformers Experience More ❯

and a good understanding of data consistency trade-offs. Proven knowledge of cloud platforms (e.g., AWS, Azure, or GCP). A Bonus: Experience with graph databases such as Neo4j, Pinecone, or Milvus. Experience building native desktop apps. Experience with NLP libraries and frameworks, such as spaCy or Transformers. Familiarity with machine learning concepts and the ability to work with NLP More ❯

monitoring. Full-Stack Integration : Develop 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 or Hugging Face Transformers Experience More ❯

Experience building with generative AI applications in production environments. Expertise with microservices architecture and RESTful APIs. Solid understanding of database technologies such as PostgreSQL and vector databases as Elastic, Pinecone, Weaviate, or similar. Familiarity with cloud platforms (AWS, GCP, etc.) and containerized environments (Docker, Kubernetes). You are committed to writing clean, maintainable, and scalable code, following best practices in More ❯

Experience building with generative AI applications in production environments. Expertise with microservices architecture and RESTful APIs. Solid understanding of database technologies such as PostgreSQL and vector databases as Elastic, Pinecone, Weaviate, or similar. Familiarity with cloud platforms (AWS, GCP, etc.) and containerized environments (Docker, Kubernetes). You are committed to writing clean, maintainable, and scalable code, following best practices in More ❯

day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices … teams to iterate fast and deliver impact Embed security, GDPR compliance, and testing best practices into the core of our stack Tech Stack Python • FastAPI • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • GitHub Actions • LangChain/LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background More ❯

day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices … teams to iterate fast and deliver impact Embed security, GDPR compliance, and testing best practices into the core of our stack Tech Stack Python • FastAPI • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • GitHub Actions • LangChain/LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background More ❯

day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices … teams to iterate fast and deliver impact Embed security, GDPR compliance, and testing best practices into the core of our stack Tech Stack Python • FastAPI • PostgreSQL + PGVector • Redis • Pinecone/Weaviate • Vertex AI • Cloud Run • Docker • Terraform • GitHub Actions • LangChain/LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background More ❯

CI/CD : Experience with 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 Git and version control platforms More ❯

production Hands-on experience with frameworks like LangChain, LangGraph, or custom-built agent orchestration setups Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, etc.), embedding stores, retrieval pipelines (e.g. Weaviate, Pinecone), and eval tooling Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains Bonus: interest or experience in robotics, mechanical/aerospace workflows, or More ❯

. Hands-on experience with LLM orchestration and prompt engineering frameworks such as LangChain or LangGraph, plus designing retrieval-augmented generation (RAG) pipelines. Familiarity with vector databases like Qdrant, Pinecone, or Redis for low-latency AI retrieval. Experience deploying, monitoring, and scaling AI workloads on cloud platforms such as AWS, GCP, or BigQuery. Bonus points for experience with Go , containerization More ❯

production Hands-on experience with frameworks like LangChain, LangGraph, or custom-built agent orchestration setups Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, etc.), embedding stores, retrieval pipelines (e.g. Weaviate, Pinecone), and eval tooling Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains Bonus: interest or experience in robotics, mechanical/aerospace workflows, or More ❯