12 of 12 LlamaIndex Jobs in the UK excluding London

hands-on experience building models and data pipelines Proven experience working with Generative AI (LLMs, prompt engineering, embeddings) Experience using frameworks such as LangChain, LlamaIndex, or similar Experience integrating with model APIs (e.g. OpenAI or open-source alternatives) Exposure to cloud environments (AWS, GCP or Azure) Strong problem-solving skills ...

emerging LLM architectures. GenAI Frameworks & Platforms: Proficient with Data & AI platforms (Azure AI, Vertex AI, Databricks, Hugging Face), advanced GenAI frameworks (LangChain, HF Transformers, LlamaIndex) & Agentic architectures (LangGraph, SmolAgents, PydanticAI) Vector DBs & RAG: Designed solutions using vector DBs & Retrieval Augmented GenAI (RAG) for knowledge applications. GenAI ModelOps/MLOps & Governance ...

emerging LLM architectures. GenAI Frameworks & Platforms: Proficient with Data & AI platforms (Azure AI, Vertex AI, Databricks, Hugging Face), advanced GenAI frameworks (LangChain, HF Transformers, LlamaIndex) & Agentic architectures (LangGraph, SmolAgents, PydanticAI) Vector DBs & RAG: Designed solutions using vector DBs & Retrieval Augmented GenAI (RAG) for knowledge applications. GenAI ModelOps/MLOps & Governance ...

emerging LLM architectures. GenAI Frameworks & Platforms: Proficient with Data & AI platforms (Azure AI, Vertex AI, Databricks, Hugging Face), advanced GenAI frameworks (LangChain, HF Transformers, LlamaIndex) & Agentic architectures (LangGraph, SmolAgents, PydanticAI) Vector DBs & RAG: Designed solutions using vector DBs & Retrieval Augmented GenAI (RAG) for knowledge applications. GenAI ModelOps/MLOps & Governance ...

Search, Pinecone, Qdrant, Weaviate and Chroma, and tuning embedding models for enterprise data. Building agentic systems using frameworks like LangGraph, Semantic Kernel, LangChain, LlamaIndex and AutoGen tool use, function calling, multi-step reasoning and orchestration. Integrating foundation models via Azure OpenAI, AWS Bedrock and open-weight model endpoints (Llama, Mistral ...

Search, Pinecone, Qdrant, Weaviate and Chroma, and tuning embedding models for enterprise data. Building agentic systems using frameworks like LangGraph, Semantic Kernel, LangChain, LlamaIndex and AutoGen tool use, function calling, multi-step reasoning and orchestration. Integrating foundation models via Azure OpenAI, AWS Bedrock and open-weight model endpoints (Llama, Mistral ...

systems and real-world applications. Key skills and experience include: Strong Python development experience Hands-on experience with LLMs and agent frameworks (e.g. LangChain, LlamaIndex, AutoGen or similar) Understanding of prompt engineering, RAG pipelines, and tool-using AI systems Experience working with APIs, data pipelines, and cloud platforms (AWS, Azure ...

systems and real-world applications. Key skills and experience include: Strong Python development experience Hands-on experience with LLMs and agent frameworks (e.g. LangChain, LlamaIndex, AutoGen or similar) Understanding of prompt engineering, RAG pipelines, and tool-using AI systems Experience working with APIs, data pipelines, and cloud platforms (AWS, Azure ...

Skills Python, TypeScript LLM frameworks (LangChain, LlamaIndex, OpenAI tools, Semantic Kernel) Vector databases (Pinecone, Weaviate, Chroma) Cloud (AWS/Azure/GCP) Docker, Kubernetes CI/CD, observability, testing for AI systems Experience Strong experience building LLM applications, RAG systems, or AI agents Understanding of prompt engineering, tool-calling ...

models and LLMs (e.g., GPT, LLaMA, Mistral, Claude). Skills in fine-tuning, prompt engineering, and building RAG pipelines. Familiarity with Agent Frameworks (LangChain, LlamaIndex, CrewAI, AutoGen). Knowledge of reinforcement learning methods or tools (Q-learning, policy gradients, RLlib). Why Join Us Be part of a cutting-edge ...

someone with deep hands-on experience building Retrieval-Augmented Generation (RAG) pipelines and Agentic frameworks . You should know how to use LangChain or LlamaIndex to create AI that can execute multi-step tasks. The Data Architect: Proficiency in Vector Databases is essential. You should be comfortable designing data persistence ...

engineering (not purely academic/research). Strong hands-on experience with LLMs in production: prompt engineering, fine-tuning, evaluation, and orchestration (e.g. LangChain, LlamaIndex, or custom pipelines). Experience designing or building agentic systems – multi-step reasoning, tool use, planning, and autonomous task execution. Proficiency in Python; experience with ...