76 to 100 of 307 Retrieval-Augmented Generation Jobs in England

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

clinicians to make faster, evidence-based decisions at the bedside. We’re developing a cutting-edge platform that combines LLMs , retrieval-augmented generation (RAG) , and vector search infrastructure to deliver real-time clinical insights. As the Founding Backend Engineer , you’ll play a critical role in shaping our backend systems, architecture, and engineering culture … from the ground up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP More ❯

clinicians to make faster, evidence-based decisions at the bedside. We’re developing a cutting-edge platform that combines LLMs , retrieval-augmented generation (RAG) , and vector search infrastructure to deliver real-time clinical insights. As the Founding Backend Engineer , you’ll play a critical role in shaping our backend systems, architecture, and engineering culture … from the ground up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP More ❯

clinicians to make faster, evidence-based decisions at the bedside. We’re developing a cutting-edge platform that combines LLMs , retrieval-augmented generation (RAG) , and vector search infrastructure to deliver real-time clinical insights. As the Founding Backend Engineer , you’ll play a critical role in shaping our backend systems, architecture, and engineering culture … from the ground up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP More ❯

with a world-class team of engineers, researchers, and product thinkers to bring AI features to life. Design and maintain retrieval-augmented generation (RAG) and agentic systems that power real-world use cases. Stay ahead of the curve by experimenting with the latest in AI research and tooling—and bring those ideas to production. … passion for solving hard problems with elegant code. Excellent communication skills—you can explain complex ideas clearly to both technical and non-technical audiences. 💡 Bonus Points For Experience with RAG pipelines and improving retrieval performance. Contributions to open-source AI projects. A portfolio of AI-powered products or prototypes you’ve helped bring to life. 🚀 Why You’ll More ❯

with a world-class team of engineers, researchers, and product thinkers to bring AI features to life. Design and maintain retrieval-augmented generation (RAG) and agentic systems that power real-world use cases. Stay ahead of the curve by experimenting with the latest in AI research and tooling—and bring those ideas to production. … passion for solving hard problems with elegant code. Excellent communication skills—you can explain complex ideas clearly to both technical and non-technical audiences. 💡 Bonus Points For Experience with RAG pipelines and improving retrieval performance. Contributions to open-source AI projects. A portfolio of AI-powered products or prototypes you’ve helped bring to life. 🚀 Why You’ll More ❯

infrastructure Working knowledge of Kubernetes, security best practices, and cloud platforms (AWS, GCP, or Azure) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with tools such as LangChain, LangSmith, or Chainlit More ❯

infrastructure Working knowledge of Kubernetes, security best practices, and cloud platforms (AWS, GCP, or Azure) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with tools such as LangChain, LangSmith, or Chainlit More ❯

integrated into enterprise applications to enhance user experience, decision-making, and automation. Exposure to modern AI application patterns such as: Retrieval-Augmented Generation (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 More ❯

integrated into enterprise applications to enhance user experience, decision-making, and automation. Exposure to modern AI application patterns such as: Retrieval-Augmented Generation (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 More ❯

integrated into enterprise applications to enhance user experience, decision-making, and automation. Exposure to modern AI application patterns such as: Retrieval-Augmented Generation (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 More ❯

Language Model (LLM) that drives Alexa+. We work on recent advances in LLM science including automated prompt optimisation, supervised fine tuning, reinforcement learning, model distillation, retrieval augmented generation, model & prompt interpretability and LLM driven data generation. The techniques are used to tune a foundational model to align with Alexa's policies on Responsible AI More ❯

growth and investment, specifically within our high-priority systematic trading and analytics systems. We are seeking a hands-on Engineering Lead to drive the development of our next-generation quantitative development platform serving the Equities Cash, Derivatives, and Prime businesses. This is a unique opportunity to make a significant impact as the platform enters its key growth phase. … Docker, Kubernetes) Proven success in enhancing developer experience that reduces friction in coding, building and deploying APIs and client libraries. Real-world application of generative AI prompt engineering and RAG pipelines. Full-stack HTML5 web development skills. Desired Skills: Understanding of Equities Cash, D1 & Deriv market mechanics and products via sell-side trading projects Familiarity with low-latency programming languages More ❯

Practical experience calling LLMs via APIs and dealing with varied responses Bonus skills Private equity or financial services experience Azure, Postgres, Streamlit or equivalents Fact extraction, Q&A and RAG on documents Comfort working in small teams with fast iteration cycles More ❯

Proficient in problem-solving and analytical reasoning. Exceptional communication and collaboration skills. Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX. Experience with Large Language Models, including RAG and fine-tuning techniques. Familiarity with compute infrastructure necessary to support operating AI and ML technology. More ❯

with a world-class team of engineers, researchers, and product thinkers to bring AI features to life. Design and maintain retrieval-augmented generation (RAG) and agentic systems that power real-world use cases. Stay ahead of the curve by experimenting with the latest in AI research and tooling and bring those ideas to production. … passion for solving hard problems with elegant code. Excellent communication skills you can explain complex ideas clearly to both technical and non-technical audiences. Bonus Points For Experience with RAG pipelines and improving retrieval performance. Contributions to open-source AI projects. A portfolio of AI-powered products or prototypes you ve helped bring to life. Why You ll More ❯

Design and develop intelligent systems leveraging agentic AI concepts Integrate advanced machine learning models with reasoning, planning, and interaction modules Utilise prompt engineering, vector databases, and RAG (Retrieval-Augmented Generation) architectures Develop and deploy solutions using agent libraries such as Lang Chain, Lang Graph, and Autogen Apply computer vision and document processing techniques to … with cross-functional teams to implement scalable AI solutions Experience: Strong proficiency in Python programming Experience with large language models (LLMs) and prompt engineering Knowledge of vector databases and RAG architecture Hands-on experience with agentic libraries such as Lang Chain, Lang Graph, and Autogen Skilled in computer vision and document processing techniques Excellent problem-solving and system design skills More ❯

Design and develop intelligent systems leveraging agentic AI concepts Integrate advanced machine learning models with reasoning, planning, and interaction modules Utilise prompt engineering, vector databases, and RAG (Retrieval-Augmented Generation) architectures Develop and deploy solutions using agent libraries such as Lang Chain, Lang Graph, and Autogen Apply computer vision and document processing techniques to … with cross-functional teams to implement scalable AI solutions Experience: Strong proficiency in Python programming Experience with large language models (LLMs) and prompt engineering Knowledge of vector databases and RAG architecture Hands-on experience with agentic libraries such as Lang Chain, Lang Graph, and Autogen Skilled in computer vision and document processing techniques Excellent problem-solving and system design skills More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs—with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

Anthropic, and Gemini. Implementing and managing multi-API workflows using tools like LiteLLM to ensure flexibility and resilience. Building sophisticated Retrieval-Augmented Generation (RAG) systems, leveraging advanced techniques like embeddings with Voyage AI, rerankers , and query enrichment . Designing and maintaining efficient data pipelines and vector storage solutions using MongoDB Atlas Vector Search. Fine … and deep learning frameworks, particularly PyTorch. Proven experience building and deploying applications with LLM APIs such as OpenAI , Anthropic , Gemini , and DeepSeek . Hands-on experience with the full RAG pipeline, including vector embeddings , rerankers , and data indexing in databases like MongoDB. Practical knowledge of LLM fine-tuning, prompt engineering, and performance optimization. Familiarity with MLOps principles and tools, including More ❯