26 to 50 of 241 Retrieval-Augmented Generation Jobs in the UK

support our AI & data science solutions. This includes the application of Generative AI technologies, such as large language models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, and prompt engineering-to build intelligent tools and enhance knowledge-based workflows. This is a fantastic opportunity for someone with foundational analytical expertise, eager to learn and grow in … client projects. You will be involved in: Applying data science best practices and standards across projects. Assisting in the design and implementation of Generative AI applications, including LLM workflows, RAG architectures, and prompt engineering. Collaborating with stakeholders to identify opportunities where GenAI can streamline tasks, automate insights, or improve decision-making. Gathering, processing, and managing data from disparate sources, ensuring … including ETL pipelines. Skills in data analysis, visualization, and storytelling with data. Analytical problem-solving and critical thinking abilities. Awareness of Generative AI techniques, including LLMs, prompt engineering, and RAG approaches. Experience with tools/frameworks for building Generative AI applications (e.g., agentic AI, orchestration frameworks, embedding models, vector databases). Understanding of machine learning and predictive modelling. Proficiency in More ❯

/or large datasets, as well as GenAI. Discover and introduce new technologies to the team, staying up to date with the latest approaches that enable the next generation of Experians products with GenAI and ML. Learn quickly and be able to put new GenAI concepts into practice. Spend 10% of your work time on continuous learning and … learning frameworks and toolkits (e.g., scikit-learn, XGBoost, TensorFlow). Hands-on experience building GenAI solutions using patterns such as Retrieval-Augmented Generation (RAG) or fine-tuning Large Language Models (LLMs). Have experience with cloud computing platforms. Greater familiarity with AWS compared to other cloud computing platforms. Qualifications Have experience in Python Are More ❯

include: Integrate AI into the SDLC by automating key engineering processes—including requirements gathering, design, coding, testing, and deployment—using tools like OpenAI Codex, ChatGPT Enterprise, Claude, OpenRouter, and RAG-based systems. Streamline Dev Workflows by embedding LLMs and RAG pipelines into daily development operations for smarter, faster delivery. Drive DevOps Automation by partnering with DevOps and SaaS leadership to … Matters by collaborating with Internal Audit to track AI performance gains and quantify automation ROI. Build AI-First Internal Tools by developing intelligent tools like prompt libraries, test generators, RAG-powered knowledge bots, and automated documentation writers. Lead on AI Integration by providing hands-on technical leadership in AI/LLM strategy—covering APIs, prompt design, and inference cost management. … LLM agents and tools into real-world engineering or product organizations Deep expertise with modern AI ecosystems, including: LangChain OpenRouter Retrieval-Augmented Generation (RAG) pipelines OpenWebUI and other dev-facing AI interfaces Background in or strong familiarity with AI-native companies Hands-on ability to own strategy and execution: from vision to POCs to More ❯

include: Integrate AI into the SDLC by automating key engineering processes—including requirements gathering, design, coding, testing, and deployment—using tools like OpenAI Codex, ChatGPT Enterprise, Claude, OpenRouter, and RAG-based systems. Streamline Dev Workflows by embedding LLMs and RAG pipelines into daily development operations for smarter, faster delivery. Drive DevOps Automation by partnering with DevOps and SaaS leadership to … Matters by collaborating with Internal Audit to track AI performance gains and quantify automation ROI. Build AI-First Internal Tools by developing intelligent tools like prompt libraries, test generators, RAG-powered knowledge bots, and automated documentation writers. Lead on AI Integration by providing hands-on technical leadership in AI/LLM strategy—covering APIs, prompt design, and inference cost management. … LLM agents and tools into real-world engineering or product organizations Deep expertise with modern AI ecosystems, including: LangChain OpenRouter Retrieval-Augmented Generation (RAG) pipelines OpenWebUI and other dev-facing AI interfaces Background in or strong familiarity with AI-native companies Hands-on ability to own strategy and execution: from vision to POCs to More ❯

automate complex legal workflows and enhance user experiences. Advanced Technology Integration: Collaborate on projects that leverage emerging technologies - such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs - to enhance our core product and explore new use cases. Cross-Functional Collaboration: Work closely with cross-functional teams to integrate advanced ML models and NLP solutions More ❯

and Experience : Exposure to hardware design or verification processes (e.g., RTL, formal verification, simulation workflows). Familiarity with vector search, retrieval-augmented generation (RAG), or multi-agent orchestration. In Return: At Arm, you'll work on AI challenges with practical impact across a global organization. You'll join a collaborative team that values deep … technical insight, clean engineering and creative problem-solving. Your contributions will influence how thousands of engineers at Arm design and verify the next generation of computing platforms-smarter, faster, and at scale. Accommodations at Arm At Arm, we want to build extraordinary teams. If you need an adjustment or an accommodation during the recruitment process, please email . 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 ❯

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 ❯

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 ❯

You'll work on projects at the cutting edge of AI, from natural language processing (NLP) and computer vision to Retrieval-Augmented Generation (RAG) and explainable AI. You'll be part of a collaborative environment where mathematical rigour meets practical innovation, building systems that make a measurable difference. What You'll Do Research & Develop … solutions for document intelligence, information retrieval, and automation. Build and enhance NLP and computer vision systems to extract, classify, and structure data from unstructured documents. Work with RAG architectures, implementing advanced document chunking, GraphRAG, and ScaNN to boost retrieval precision. Deploy AI-powered bots and web applications on cloud platforms (e.g., Microsoft Azure). Develop systems … NLP, computer vision, and retrieval methods. Ability to turn theory into practical, deployable systems. Desirable Experience with Microsoft Azure or other cloud platforms. Knowledge of vector search, RAG pipelines, and document chunking strategies. Familiarity with advanced search techniques such as anisotropic vector quantisation. Interest in explainable AI and model interpretability. Why Join Us? Work on groundbreaking AI projects 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 ❯