26 to 50 of 208 Retrieval-Augmented Generation Jobs in England

experience with cloud platforms (AWS, GCP, Azure) and MLOps workflows. Solid understanding of vector databases (e.g. Pinecone, Weaviate, Milvus) and retrieval-augmented generation (RAG) techniques. Comfort with rapid technical recall for industry benchmarks, model parameters, and framework capabilities. More ❯

experience with cloud platforms (AWS, GCP, Azure) and MLOps workflows. Solid understanding of vector databases (e.g. Pinecone, Weaviate, Milvus) and retrieval-augmented generation (RAG) techniques. Comfort with rapid technical recall for industry benchmarks, model parameters, and framework capabilities. More ❯

problem-solving skills and the ability to work autonomously in a dynamic environment. DESIRABLE SKILLS Experience supporting LLM training or retrieval-augmented generation (RAG). Familiarity with feature stores, vector databases, or embedding pipelines. Understanding of data quality, lineage, and governance best practices. Exposure to cloud platforms (AWS, Azure, or GCP) and distributed compute More ❯

with enterprise partners. No two weeks will look the same. Fine-tune and privately deploy LLMs - with a focus on Retrieval-Augmented Generation (RAG) pipelines Build and scale computer vision systems - from object detection to image segmentation Apply NLP to real-world business problems - summarisation, entity recognition, information extraction, and more Train and deploy More ❯

advanced Large Language Models and Generative AI solutions, managing the computational challenges and optimisation techniques required for enterprise-scale applications. Advanced RAG Systems : Design and implement sophisticated Retrieval-Augmented Generation systems, combining LLMs with external knowledge bases to enhance information retrieval and improve response accuracy across diverse client domains. Technical Authority & Mentorship … experience in developing, fine-tuning, and deploying Large Language Models, with deep understanding of model architectures, optimisation techniques, and computational challenges. RAG Implementation : Proven expertise in implementing Retrieval-Augmented Generation systems, with strong understanding of RAG workflows, including indexing, retrieval, and integration with language models. Technical Proficiency : Advanced proficiency in Python and More ❯

and love building innovative solutions from scratch, this could be the perfect role for you. What you'll be doing Building and refining LLM-based agents, including retrieval-augmented generation and data retrieval systems. Designing AI-powered system architecture using Python, Java, and SQL/NoSQL databases. Fine-tuning models (LLMs & SLMs … Essential: Strong experience with Gen AI, NLP, and machine learning. Proven ability to create, train, and fine-tune LLMs/SLMs. Skills in prompt engineering and RAG (retrieval-augmented generation). Solid background in Python, Java, and database technologies (SQL/NoSQL). Experience in crawling/web scraping and API development. Experience with More ❯

or PhD in Computer Science, Machine Learning, Computational Social Science, or a related quantitative field, or equivalent practical experience. (Desirable) Hands-on experience with advanced LLM application techniques like RAG, chain-of-thought, and agentic tool use. Deep experience in applying large language models (LLMs) to solve complex, open-ended problems. (Desirable) Experience designing and conducting experiments in a social … experimental design, and insights from computational social science to bring synthetic populations to life. Design AI Agents: Develop prompting strategies, retrieval-augmented generation (RAG), and tool-use frameworks for consistent personas, memories, and reasoning. Develop Experimental Methods: Design experiments to test behavioral outputs of LLM-powered agents against real data and social science principles. More ❯

infrastructure, vector databases, or search systems. Experience building ML-powered products in production. Knowledge of large language models (LLMs) and retrieval-augmented generation (RAG). Public speaking or published technical content (talks, blog posts, tutorials). Familiarity with the Qdrant ecosystem or similar technologies. Benefits Competitive compensation and equity options. Flexible remote work environment. More ❯

Skills & Expertise Experience deploying and managing applications using Azure and Docker. Familiarity with frameworks such as LangChain and expertise in Retrieval-Augmented Generation (RAG) models for AI-driven applications. Proficiency with pandas for data manipulation. Full Stack Developer - What's in it for you? Salary Reviews: Twice a year to recognise your contributions. Generous More ❯

Expertise Experience working with Large Language Models (LLMs) and understanding of trade-offs between performance, cost, and capability. Understanding of Retrieval-Augmented Generation (RAG), agent orchestration, prompt engineering, and tool calling. Familiarity with AI standards such as Model Context Protocol (MCP) and Agent2Agent (A2A). Strong knowledge or experience in working with various ML More ❯

Expertise Experience working with Large Language Models (LLMs) and understanding of trade-offs between performance, cost, and capability. Understanding of Retrieval-Augmented Generation (RAG), agent orchestration, prompt engineering, and tool calling. Familiarity with AI standards such as Model Context Protocol (MCP) and Agent2Agent (A2A). Strong knowledge or experience in working with various ML More ❯

We're Looking For 3-10 year's experience in IT strategy, consulting, or software development Prior consulting experience essential Strong technical knowledge of GenAI and ML, including LLMs, RAG, MLOps, and prompt engineering Familiarity with platforms such as AWS Bedrock, Google Vertex, LangChain, or LlamaIndex Experience with both legacy systems and modern tech stacks Proven track record in agile More ❯

Data Factory, Azure Synapse, and Azure Functions. Implementing modern retrieval techniques such as vector search, semantic search and Retrieval-Augmented Generation (RAG) using tools like Azure Cognitive Search, FAISS, Pinecone, or Weaviate. Familiarity with data governance, privacy, and ethical AI principles. Experience with DevOps tools and practices, including CI/CD pipelines More ❯

We're looking for an experienced Senior Software Engineer to join our team driving innovation on Mintel Leap, our next-generation platform powered by AI and large language models (LLMs). This role is ideal for someone passionate about building intelligent, scalable systems and user-centric interfaces. You'll play a key role in developing consistent, accessible user … backend services that enable cross-product functionality. You'll also contribute to the integration of cutting-edge AI capabilities, including Retrieval-Augmented Generation (RAG), to deliver smarter, more efficient, and more reliable experiences across our products. Why join Mintel? We are the world's leading market intelligence agency and technology underpins our success. We … them on AWS. Developers who are confident with front-end development. Our more recent front-ends use React and Typescript. Collaborate with AI focused engineers to integrate LLMs and RAG pipelines into production systems. Candidates who are able to quickly pick up domain knowledge and effectively up-skill on technology topics where required. Applicants who are keen to contribute to More ❯

Microservices, and AI integrations. Oversee LLM (Large Language Model) integration, leveraging models like GPT, and implement advanced AIarchitectures such as Retrieval-Augmented Generation (RAG) and Reinforcement Learning with HumanFeedback (RLHF). Lead the development of applications using React, .NET, Python, or Node.js, ensuring scalable, maintainable, andperformant solutions. Team Management: Manage and mentor engineering managers … React for front-end development. Deep knowledge of cloud technologies (AWS, Azure) and experience deploying cloud-native applications and Microservices architectures. Strong background in AI integration, specifically LLM technologies, RAG, and RLHF architectures, and the ability to apply these in client-facing solutions. Solid understanding of system design principles, including scalability, high availability, performance and security. Leadership & Client Engagement: Proven More ❯

and AI integrations. Oversee LLM (Large Language Model) integration, leveraging models like GPT, and implement advanced AI architectures such as Retrieval-Augmented Generation (RAG) and Reinforcement Learning with Human Feedback (RLHF). Lead the development of applications using React, .NET, Python, or Node.js, ensuring scalable, maintainable, and performant solutions. Team Management: Manage and mentor … React for front-end development. Deep knowledge of cloud technologies (AWS, Azure) and experience deploying cloud-native applications and microservices architectures. Strong background in AI integration, specifically LLM technologies, RAG, and RLHF architectures, and the ability to apply these in client-facing solutions. Solid understanding of system design principles, including scalability, high availability, performance, and security. Leadership & Client Engagement: Proven More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

impact and are excited about working in a high-growth startup. Nice to have: experience with Vector databases, and implementing retrieval-augmented generation (RAG) systems and data pipelines Nice to have: exposure to evaluation systems, including running experiments, labeling, user feedback, and scoring methods. Note: If you feel you don't have the whole More ❯

is an exciting opportunity to work in a greenfield environment, where you will play a crucial role in designing and deploying secure, scalable AI services that drive next-generation use cases, including client intelligence, document processing, and risk management. Key Responsibilities: Architect & Implement: Design and deploy secure AI services from lab to … production, ensuring they are scalable and compliant with industry standards. API Development: Create robust APIs for large language models (LLMs), retrieval-augmented generation (RAG) pipelines, agentic workflows, and document intelligence systems. Cybersecurity & Privacy: Integrate cybersecurity and data privacy controls across all AI workflows, including encryption, anonymisation, and access logging. Collaborate with CISO: Work closely … workflows using modular LLM agents with memory, planning, and tool integration. Experience implementing Model Context Protocol (MCP) for secure, auditable context injection across agentic systems. Demonstrated ability to build RAG pipelines with strict data governance and contextual integrity. Familiarity with regulatory frameworks such as the EU AI Act, FCA cybersecurity principles, and oversight of critical systems. Previous collaboration with cybersecurity More ❯

is an exciting opportunity to work in a greenfield environment, where you will play a crucial role in designing and deploying secure, scalable AI services that drive next-generation use cases, including client intelligence, document processing, and risk management. Key Responsibilities: Architect & Implement: Design and deploy secure AI services from lab to … production, ensuring they are scalable and compliant with industry standards. API Development: Create robust APIs for large language models (LLMs), retrieval-augmented generation (RAG) pipelines, agentic workflows, and document intelligence systems. Cybersecurity & Privacy: Integrate cybersecurity and data privacy controls across all AI workflows, including encryption, anonymisation, and access logging. Collaborate with CISO: Work closely … workflows using modular LLM agents with memory, planning, and tool integration. Experience implementing Model Context Protocol (MCP) for secure, auditable context injection across agentic systems. Demonstrated ability to build RAG pipelines with strict data governance and contextual integrity. Familiarity with regulatory frameworks such as the EU AI Act, FCA cybersecurity principles, and oversight of critical systems. Previous collaboration with cybersecurity More ❯

transport industry and/or geographical information systems (GIS) Experience with cloud infrastructure Understanding of NLP algorithms and techniques and/or experience with Large Language Models (fine tuning, RAG, agents) Experience with graph technology and/or algorithms Our technology stack Python and associated ML/DS libraries (scikit-learn, numpy, LightlGBM, Pandas, LangChain/LangGraph TensorFlow, etc ) PySpark More ❯