1 to 25 of 31 Remote Retrieval-Augmented Generation Jobs in the UK

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

needed Our client is an international company that requires a senior Data Scientist with experience in Azure Databricks, Knowledge Graph, Neo4J Graph Database, and RAG pipelines for LLM to join the team. Job Description: Responsibilities: Develop and implement data models and algorithms to solve complex business problems. Utilize Databricks to More ❯

Ideally AWS) Strong Python & SQL experience Experience in advertising, marketing or YouTube/social media video (Highly Desirable) Experience or understanding of LLMS, LangChain, & RAG (Beneficial) Sound of interest? Apply today for immediate consideration! Please note: All candidates must be UK based and have the full right to work in More ❯

Ideally AWS) Strong Python & SQL experience Experience in advertising, marketing or YouTube/social media video (Highly Desirable) Experience or understanding of LLMS, LangChain, & RAG (Beneficial) Sound of interest? Apply today for immediate consideration! Please note: All candidates must be UK based and have the full right to work in More ❯

Instruction Tuning, Reinforcement Learning Alignment, Distributed Training, Efficient ML techniques) Post-training techniques for planning & reasoning (e.g. LLMs & Knowledge Graphs, Advanced Prompting, CoT pipelines, RAG, Tool use, etc.) Data-centric Machine Learning (Synthetic Data, Curriculum Learning, learned data-mixtures) Evaluation (Benchmarks, Human-in-the-loop, red teaming/Adversarial Testing More ❯

Weights & Biases. A solid background in software engineering and DevOps practices, MLOps deployment, and infrastructure. A knack for generative AI frameworks and SDKs, like RAG, Langchain/LangGraph, Semantic Kernel, and tools such as MS tooling, Co-Pilot Studio, ML Studio, Prompt flow, Kedro, etc. Proficiency with pipeline orchestration tools More ❯

current with the Foundation Model ecosystem and applying these in practical, innovative ways. Experienced with Large Language Model projects, including API integration, prompt engineering, RAG, fine-tuning, and AI project management. From an engineering background or capable of building technical solutions with strong engineering principles for robustness, trustworthiness, and scalability. More ❯

novel, real world use cases. Experience delivering Large Language Model projects with customers, including LLM API integration, current knowledge of foundation models, prompt engineering, RAG, fine tuning, and managing AI projects. You either come from an Engineering background, or your desire to ship working solutions has allowed you to build More ❯

stack: Python, LlamaIndex/LangChain, PyTorch, HuggingFace, FastAPI, Postgres, SQLAlchemy, Alembic, OpenAI, Docker, Azure, Typescript, React. You have hands-on experience working with LLMs, RAG architectures, NLP, or applying Machine Learning to solve real-world problems. You have a strong track record of building datasets for training and/or More ❯

scalable GenAI pipelines that generate high-quality content, from product descriptions, reviews, titles, and other product content. Design and evaluate prompt tuning strategies and RAG systems to ensure factual and engaging outputs. Fine-tune foundation models and develop domain-specific adapters using techniques like LoRA, PEFT, and instruction tuning. Define More ❯

scalable GenAI pipelines that generate high-quality content, from product descriptions, reviews, titles, and other product content. Design and evaluate prompt tuning strategies and RAG systems to ensure factual and engaging outputs. Fine-tune foundation models and develop domain-specific adapters using techniques like LoRA, PEFT, and instruction tuning. Define More ❯

language capabilities of large language models (LLMs) with the logical reasoning power of symbolic AI. This role is crucial in developing the next generation of decision intelligence for high stakes applications, where explainability, determinism, and precision are key. Role Specification As an R&D Engineer at Rainbird, you … evolution. The role demands expertise in LLM optimization techniques including fine-tuning on domain-specific data, crafting robust prompting strategies, and implementing retrieval-augmented generation style architectures. You'll apply these techniques to enhance decision accuracy while maintaining deterministic behavior where required. Your work … Huggingface), and a solid grasp of natural language processing techniques to enhance machine understanding and interaction. Knowledge of vector databases, embeddings, and retrieval-augmented generation style architectures. A proven track record of conducting rigorous research and translating theoretical findings into practical, solutions that drive More ❯

our analytics) OCR engines (we use AWS Textract, GDocAI, and we have used tesseractOCR in the past) Prompt Engineering Weaviate (we use it for RAG in LLM powered tasks and for hybrid searches) Kubernetes (we run Weaviate and other specific services on Kubernetes) CircleCI DataDog Auth0 (we use it, but More ❯

run evaluation experiments (precision/recall, BLEU, CSAT, etc.), then turn findings into actionable model tweaks. . Optimise prompts, retrieval-augmented-generation (RAG) chains and guard-rails to meet healthcare compliance standards (GDPR, ISO 27001, NHS DSPT). . Define a repeatable training pipeline More ❯

years+) Developed LLM architecture and deployed LLM applications Effective communication skills Uptodate with current trends in AI Some experience with applying latest techniques like RAG architecture, GenAI, Parallel training etc The role is hybrid, with requirements to be on client premises' in either Glasgow or Edinburgh between 1-4 days More ❯

Engineering skills (3 years+) Developed LLM architecture and deployed LLM applications Uptodate with current trends in AI Some experience with applying latest techniques like RAG architecture, GenAI, Parallel training etc The role is hybrid, with adhoc requirements to be on client premises (London) this could be between 1-5 days More ❯

Engineering skills (3 years+) Developed LLM architecture and deployed LLM applications Uptodate with current trends in AI Some experience with applying latest techniques like RAG architecture, GenAI, Parallel training etc The role is hybrid, with adhoc requirements to be on client premises (London) this could be between 1-5 days More ❯

the value of an organization's back office data through the automation of end-to-end processes, and transforms complex documents into LLM and RAG-ready data to power enterprise GenAI experiences. This enables organizations to transform manual, siloed processes into a strategic advantage, resulting in a faster path to More ❯

Previous experience in AI powered enterprise/B2C SaaS environment and product. Familiarity with low-code/no-code AI tools, vector databases, or RAG workflows. Experience launching AI-powered features or agent-led automation tools. Background as a software engineer or technical role Please reach out for more information More ❯

development. Minimum Requirements: +6 years of experience working with Node.js/JavaScript. AI Development Experience: Proficient in working on AI-related applications such as RAG, Agents, Inference, fine-tuning, and training. Microservices Architecture: Familiarity with microservices architecture for scalable applications. Peer-to-Peer Technologies: Understanding of Peer-to-Peer technologies. More ❯