1 to 25 of 135 Retrieval-Augmented Generation Jobs in the UK

looking for a highly skilled Lead AI Solutions Engineer to design, build, and optimize a Retrieval-Augmented Generation (RAG) system that underpins our self-serve analytics data applications. In this pivotal position, you will develop a scalable RAG platform, integrate multiple data sources, and … create intuitive data interactions to empower teams across the organization. As part of our Group's RAG Initiative, you will collaborate with a cross-functional team to implement self-service AI-driven solutions - ranging from NLP Data Analysis and Data Discovery to other analytics assistants we will be deploying to … s about reshaping our operational DNA to drive innovation, efficiency, and exceptional customer experiences in the new era of analytics and automation. Key Responsibilities: RAG System Development Enhance, build new assistants and maintain existing scalable and modular RAG architectures for data retrieval and generation. Develop APIs and microservices More ❯

interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join … relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models … skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments. Full-stack experience (React, TypeScript, Node.js) and API development. Familiarity with vector search More ❯

to redefine primary care while helping people live happier, healthier, and longer. Education Masters Degree About the Role You will design, build, and optimize RAG pipelines that combine large language models (LLMs) with domain-specific retrieval systems, enabling natural language understanding and reasoning over patient data, clinical guidelines … work will directly impact how patients and clinicians interact with our platform, enabling safe, accurate, and context-aware content surfacing. Responsibilities Design and implement RAG architectures using open-source and potentially proprietary LLMs (e.g., LLaMA, Mistral, OpenAI, Anthropic). Build and maintain retrieval pipelines over structured and unstructured … notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely with product, clinical, and data science teams to fine-tune More ❯

to redefine primary care while helping people live happier, healthier, and longer. Education Masters Degree About the Role You will design, build, and optimize RAG pipelines that combine large language models (LLMs) with domain-specific retrieval systems, enabling natural language understanding and reasoning over patient data, clinical guidelines … work will directly impact how patients and clinicians interact with our platform, enabling safe, accurate, and context-aware content surfacing. Responsibilities Design and implement RAG architectures using open-source and potentially proprietary LLMs (e.g., LLaMA, Mistral, OpenAI, Anthropic). Build and maintain retrieval pipelines over structured and unstructured … notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely with product, clinical, and data science teams to fine-tune More ❯

with experience in deploying and leading complex AI/ML workloads. Implemented production applications using Retrieval-augmented generation (RAG) concepts and managed large Knowledge Bases (KBs) with embeddings, chunking and other optimization techniques within VectorDBs. Expert programming skills in languages such as Python, Java More ❯

AI initiatives span categorization, summarization, data analysis, automated content generation, and rich media analysis - leveraging both existing AI/ML tools (LLMs, RAG, embeddings, agentic workflows) and custom solutions. You'll collaborate across teams to build practical, scalable AI solutions - where precision matters. With a "you build it … Select, train, and fine-tune AI models based on need, leveraging appropriate techniques (e.g. LLMs, Retrieval-Augmented Generation (RAG), Vector Databases, Embeddings, Multi-modal AI, Agentic Workflows) (Model Development). Integrate and deploy scalable AI based applications, backend services and APIs (Deployment). Develop More ❯

you will architect robust AI workflows, craft precise prompts and implement advanced techniques such as Retrieval-Augmented Generation (RAG), vector stores and graph-based solutions. Working closely with cross-functional teams, you will be pivotal in transforming complex data challenges into scalable, high- impact … Architect and orchestrate complex AI workflows to drive process automation. Design, optimise and fine-tune prompts to maximise model performance. Implement advanced methods including RAG, vector stores (e.g., FAISS, ASTRA) and graph-based systems. Collaborate with product, engineering and data science teams to ensure seamless deployment and scalability of AI … Python programming skills, with proficiency in frameworks such as TensorFlow and PyTorch. Proven expertise in AI workflow orchestration and prompt engineering. Solid understanding of RAG, vector storage solutions and graph-based methodologies. Minimum of 2 years' experience in AI/ML development, supported by a degree in Computer Science or More ❯

experiment, and ship new features. Explore and implement advanced concepts such as multi-agent systems, retrieval-augmented generation (RAG), and agentic architectures. Champion quality and reuse across the product and the codebase. Work across the business to ensure the features you develop have a More ❯

for intelligent data discovery, extraction, and decision-making. Oversee the integration of LLMs, knowledge graphs, retrieval-augmented generation (RAG), and reinforcement learning for autonomous task execution. Champion the application of explainable and trustworthy AI principles in all solutions. Hands-on Technical Leadership Serve as … leadership responsibility (people management experience not required). Deep expertise in building intelligent systems with one or more of the following: LLMs, agent frameworks, RAG, knowledge representation, or autonomous data pipelines. Strong programming and software engineering skills (Python preferred; familiarity with cloud-native and MLOps tools). Demonstrated experience in More ❯

NLP), Generative AI, Multimodal Large Language Model (MLLM), Natural Language Understanding (NLU), Machine Learning (ML), Retrieval-Augmented Generation (RAG), Computer Vision, Responsible AI, LLM Agents, Evaluation, and Model Adaptation. Key job responsibilities As an Applied Scientist on our team, you will be responsible for … test experiments, optimize and deploy your models into production, working closely with software engineers. Establish automated processes for large-scale data analysis and generation, machine-learning model development, model validation and serving. Communicate results and insights to both technical and non-technical audiences, including through presentations and written More ❯

LLM technologies and apply them to production environments, including third-party and open-source LLMs, Retrieval-Augmented Generation (RAG), model fine-tuning, structured output, and external connectors. Collaborate with instructional designers to refine and optimize prompt engineering for enhanced learning outcomes. Develop and maintain More ❯

high-performance machine learning models to power AI-driven products. Design and operate large-scale ML systems, including LLM gateways, batch inference pipelines, and RAG-powered applications. Drive MLOps and LLMOps best practices across the organization, ensuring scalability, reliability, and efficiency. Lead the development of custom LLM architectures, pre-training … architecture, particularly around microservices and cloud platforms (e.g., AWS, GCP) Experience building, implementing and optimising Retrieval-Augmented Generation (RAG) models to enhance AI applications. A "can-do" attitude, thrive in high-pressure environments, and communicate clearly and proactively with both technical and non-technical More ❯

high-performance machine learning models to power AI-driven products. Design and operate large-scale ML systems, including LLM gateways, batch inference pipelines, and RAG-powered applications. Drive MLOps and LLMOps best practices across the organization, ensuring scalability, reliability, and efficiency. Lead the development of custom LLM architectures, pre-training … particularly around microservices and cloud platforms (e.g., AWS, GCP). Experience building, implementing and optimising Retrieval-Augmented Generation (RAG) models to enhance AI applications. A "can-do" attitude, thrive in high-pressure environments, and communicate clearly and proactively with both technical and non-technical More ❯

systems for high-speed, accurate search functionality. • Explore and implement advanced techniques, such as Retrieval-Augmented Generation (RAG), to enhance AI capabilities. • Conduct experiments to measure and improve the performance of NLP models. • Build custom language models tailored to specific industry requirements. What More ❯

appropriate trade-o s in design and architecture, particularly when integrating Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) workflows, and other advanced AI technologies. Minimum Qualifications & Experience • 5+ years of experience as a Technical Project Manager or equivalent in managing software and AI … ideally with React and Python stacks, such as Django, FastAPI, or Flask). • Experience delivering AI-driven projects and integrating Large Language Models (LLMs), RAG workflows, or similar technologies (ideally relevant to AVIS or similar products). • End-to-end delivery experience in Agile environments, managing sprints, timelines, and budgets. More ❯

build innovative GenAI proof-of-concept (POC) solutions for clients, leveraging cutting-edge technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Evolve prototype POCs into fully scalable, production-ready solutions that meet client needs. Develop and implement FullStack applications for both GenAI and More ❯

the forefront of tech, working with advanced AI models, including large language models (LLMs) and Retrieval-Augmented Generation (RAG) techniques, gaining hands-on experience with the latest innovations. High-impact role: Your contributions will directly shape BRYTER's growth and success. Collaborative and innovative More ❯

used in automation such as REX, JCL, and YAML - Working with Large Language Models and Retrieval-Augmented Generation (RAG) models - Working with Conversational AI and working with VS Code. Possess robust communication skills, demonstrating the ability to articulate messages clearly and engage audiences effectively. More ❯

of cloud security and Identity and Access Management (IAM). Knowledge of vector databases and retrieval-augmented generation (RAG). Data pipeline development experience using Airflow, Spark, or similar tools. Experience with AWS Lambda and DynamoDB Streams. Why you'll love working at WorkBuzz More ❯

Computer Science, Mathematics or a similar quantitative discipline Understanding of NLP algorithms and techniques and/or experience with Large Language Models (fine tuning, RAG, agents) Are proficient with Python, including open-source data libraries (e.g Pandas, Numpy, Scikit learn etc.) Have experience productionising machine learning models Are an expert More ❯

implementation of AI solutions across Morningstar's business units. Requirements: Minimum 3 years of hands-on experience implementing AI/machine learning solutions, including RAG , NLP , AI Agents , and transformer models, in commercial applications. Advanced degree (Master's/PhD preferred) in a quantitative, AI or computational field such as More ❯

and optimising hyperparameters for improved performance Skilled in building LLM-powered applications for real-time decision-making and automation across various domains Familiarity with RAG and agentic workflows Solid understanding of machine learning algorithms, deep learning architectures, and their applications Strong problem-solving skills and the ability to translate complex More ❯

Prompt-Savvy : Comfortable with prompt engineering, model evaluation, and structured analysis of generative systems. Experimentation First : Experience (or interest) in fine-tuning, instruction tuning, RAG architectures, or guardrail implementation. Collaborative Communicator : Able to bridge the gap between research and product, working across teams to translate ideas into features. Curious & Agile More ❯

prototype solutions for their business challenges. This role will require a strong consultative approach coupled with critical thinking and hands-on NLP and GenAI (RAG) expertise. You'll be a key member in this team, with significant customer exposure, taking them on the journey from early adoption through to scale. … relationship with our customers. Speaking with empathy and credibility will be key. Skills and Experience required 1+ years of focused LLM/GenAI and RAG project experience, preferably in a leadership role; A data science background. NLP experience is key; Fluent in Python, and able to rapidly and independently prototype … propose how GenAI may solve business challenges; Chain of thought reasoning to build up LLM based solutions; Resource Augmented Generation (RAG) including optimisation of chunking and metadata; Follow Responsible AI principles; Rapidly develop customer demonstrations to show the art of the possible with GenAI; Contribute to More ❯

independently and collaboratively. Preferred Skills: Experience building scalable applications with LLMs, using frameworks such as LangChain, LlamaIndex, Hugging Face, etc. Depth of knowledge with RAG implementation and improvements. More ❯