1 to 25 of 48 Pinecone Jobs in the UK

Supervised/unsupervised Model tuning, 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-on mastery of core GenAI frameworks More ❯

Supervised/unsupervised Model tuning, 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-on mastery of core GenAI frameworks More ❯

principles for building scalable, maintainable, and 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 task decomposition for agents More ❯

principles for building scalable, maintainable, and 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 task decomposition for agents More ❯

QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep expertise in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma. Software & Cloud Engineering: Programming & APIs: Expert-level Python and demonstrable experience building production services with FastAPI. Cloud Platform: Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE More ❯

QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep expertise in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma. Software & Cloud Engineering: Programming & APIs: Expert-level Python and demonstrable experience building production services with FastAPI. Cloud Platform: Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE More ❯

semi-structured sources. Design RAG systems: chunking strategies, document schemas, metadata, hybrid/dense retrieval, re-ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search, pgvector, Pinecone/Weaviate). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners’ research and product offerings, including document understanding, automatic summarisation, topic modelling, semantic More ❯

FastAPI) or TypeScript (Express). 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 or Langfuse. Collaborate in agile More ❯

within a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). More ❯

within a SaaS or data-driven business. Strong knowledge of LLMs , prompt engineering, and fine-tuning approaches. Hands-on experience with AI/ML pipelines and vector databases (e.g. Pinecone, FAISS, Weaviate). Proficiency in Python plus at least one other backend language (TypeScript or Java preferred). Proven experience with AWS , containerisation, and infrastructure as code (Terraform, Docker). More ❯

FastAPI) or TypeScript (Express). 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 or Langfuse. Collaborate in agile More ❯

security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap More ❯

security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap More ❯

security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap More ❯

security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap More ❯

security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap More ❯

making processes. Master Embedding Strategies: Create and manage high-quality vector embeddings for semantic search, text classification, and other NLP tasks. You will work extensively with vector databases like Pinecone, Weaviate, or Chroma. Construct LLM Chains and Graphs: Utilize LangChain or LangGraph to develop, prototype, and productionize complex, stateful applications and workflows powered by LLMs. Model Integration & Deployment: Fine-tune More ❯

Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong experience with Appian low-code platform including process modelling, interface design, expression More ❯

Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and Ansible Experience with business process automation (RPA More ❯

and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data engineering. Familiarity with vector databases (FAISS, Pinecone, ChromaDB). Hands-on with Linux, Bash/Powershell scripting, cloud environments. Creative problem-solver with excellent communication and collaboration skills. Curious, adaptable, and passionate about staying at the edge More ❯

Azure OpenAI APIs. Knowledge of Claude (Anthropic) models and integration. Hands-on experience with LangChain , LangGraph , RAG , and MCP (Model Context Protocol) . Familiarity with vector databases (PostgreSQL Vector, Pinecone, Weaviate, etc.). Understanding of AI agent orchestration concepts and frameworks. Bonus Skills: Voice/speech processing using Librosa or similar libraries. Azure cloud deployment (App Services, Functions, Containers). More ❯

engineering Strong communicator and collaborator across technical and business teams Bonus Points Experience with NVIDIA CUDA, cuDNN, TensorRT, and GPU acceleration Familiarity with HPC workloads and vector databases (FAISS, Pinecone) Knowledge of ethical AI frameworks and global compliance standards Why HCLTech? Work on mission-critical AI systems across industries like healthcare, finance, and high-tech Collaborate with global thought leaders More ❯

semi-structured sources. Design RAG systems : chunking strategies, document schemas, metadata, hybrid/dense retrieval, re-ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search , pgvector, Pinecone/Weaviate). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners’ research and product offerings, including document understanding, automatic summarisation, topic modelling, semantic More ❯

ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to the creation of a strategic AI adoption roadmap and More ❯

ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to the creation of a strategic AI adoption roadmap and More ❯