101 to 125 of 239 Retrieval-Augmented Generation Jobs in the UK

models using unsupervised learning, deep learning (e.g. auto-encoders), and novelty detection techniques. Lead a team of Data Scientists Drive innovation in LLM-based security automation, including prompt engineering, RAG pipelines, and fine-tuning of models. Collaborate with Data Engineers to build scalable, secure, and production-ready ML pipelines on GCP. Partner with cyber defence, risk, and compliance teams to More ❯

models using unsupervised learning, deep learning (e.g. auto-encoders), and novelty detection techniques. Lead a team of Data Scientists Drive innovation in LLM-based security automation, including prompt engineering, RAG pipelines, and fine-tuning of models. Collaborate with Data Engineers to build scalable, secure, and production-ready ML pipelines on GCP. Partner with cyber defence, risk, and compliance teams to More ❯

and observability tools. Hands-on experience with languages and tools like Ruby, Java, React, GraphQL, AWS/GCP, and Kubernetes. Bonus: Experience with vector databases, LLM frameworks (e.g., LangChain, RAG), and open-source AI tools. Excellent communication, collaboration, and problem-solving skills. Strong organizational skills with a passion for continuous improvement. Agile Practices: Demonstrated success with agile product development, iterative More ❯

Data and AI strategy to executive stakeholders, customers, partners, and industry as needed. Technical Leadership Build & own common AI frameworks, AI platform and infrastructure components and multi agent architecture, RAG patterns, orchestration & governance patterns across the platform Create systems to leverage and experiment with best of breed AI innovation in the industry balancing performance, cost and quality tradeoffs Evolve process More ❯

deepen our understanding of AI and ML model behaviour, enabling the safe and effective exploitation of advanced AI. This includes contributing to initiatives such as LLM-as-a-judge, RAG evaluations, and agentic workflow assessment. To thrive in this role, you should possess a strong foundation in data science and a passion for responsible AI. We are looking for candidates More ❯

applications using modern full-stack technologies. Key Responsibilities Develop and deploy AI-powered features into production systems. Build and optimise automation pipelines for scalable delivery. Implement RAG (Retrieval-Augmented Generation) and reasoning models into customer-facing applications. Collaborate with data scientists and engineers to translate technical concepts into real-world tools. Build full-stack … MERN Stack (MongoDB, Express.js, React.js, Node.js or similar..) Strong proficiency in Python, particularly for AI and ML applications. Hands-on experience with PyTorch, GenAI, and reasoning models. Experience implementing RAG architectures in production environments. Solid understanding of both SQL and NoSQL databases Experience building and consuming APIs; familiarity with CI/CD practices. Nice to Have Exposure to cloud platforms More ❯

Pandas, Scikit-Learn, Langchain, Llamaindex, Azure AI Foundry, among others Practical experience and deep expertise with GenAI techniques such as Finetuning, Prompt engineering, prompt orchestration, retrieval methods (RAG and Knowledge graph techniques), Agentic Systems, etc. Knowledge of Agentic frameworks such as LangGraph, Azure AI Foundry Agents, Semantic Kernel Agents, etc. Knowledge of prompt orchestration and optimization techniques such More ❯

months but likely to extend paying £475 per day In order to be considered for this role you will need to have - LLM applications: LangChain, Pydantic-AI (similar frameworks), RAG systems, MCP Servers Front End: some experience with Typescript, REACT Application Servers, web Servers, database Servers, docker Front End (TS/React) and a Back End (Python/FastAPI) leveraging More ❯

Architect multi-step agent workflows using: Semantic Kernel SDK (C# or Python) Azure OpenAI (GPT-4, function calling, chat completion) Planner and Kernel Memory APIs for reasoning and memory RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in: SharePoint, OneDrive, Teams, Outlook, and Planner Use More ❯

Architect multi-step agent workflows using: Semantic Kernel SDK (C# or Python) Azure OpenAI (GPT-4, function calling, chat completion) Planner and Kernel Memory APIs for reasoning and memory RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in: SharePoint, OneDrive, Teams, Outlook, and Planner Use More ❯

MVPs, then production ready services. - Develop, package and maintain shared Python libraries; ensuring robust and clean architecture, testing and CI/CD standards. - Design and deploy LLM based extraction, RAG and agentic workflows over large document collections, and drive value through integration of latest AI tech (agentic workflows etc.) - ️ Communicate clearly with technical and non technical stakeholders. Required Skills & Experience More ❯

Shop, Sports & Social Club and More Skills Required: Proficiency in Data Science techniques, including statistical models and ML algorithms. Expertise in NLP, with a keen understanding of LLM and RAG technologies. Strong development capabilities, particularly in Python. Experience with data exchange, processing, and storage frameworks (ETL, ESB, API, SQL, NoSQL, and Parquet). Comfort with Agile development methodologies. Excellent teamwork More ❯

tools for modelling. Working experience on Modern data platforms which involves Cloud & related technologies Keeps track of industry trends and incorporates new concepts Advanced understanding of AI concepts , LLMS, RAG, Agentic apps Advance Understanding in Data visualization and Data pipelines. Excellent understanding of traditional and distributed computing paradigm. Experience in designing and building scalable data pipelines. Should have excellent knowledge More ❯

tools for modelling. Working experience on Modern data platforms which involves Cloud & related technologies Keeps track of industry trends and incorporates new concepts Advanced understanding of AI concepts , LLMS, RAG, Agentic apps Advance Understanding in Data visualization and Data pipelines. Excellent understanding of traditional and distributed computing paradigm. Experience in designing and building scalable data pipelines. Should have excellent knowledge More ❯

DevOps : Solid grasp of microservices, Docker, Kubernetes, and CI/CD pipelines (GitHub Actions, Jenkins) AI/ML Foundations : Familiarity (or eagerness to learn) LLM libraries, vector stores, and RAG paradigms Sector specific knowledge: Experience with financial data systems or forecasting models Communication & Collaboration : Skilled at articulating technical concepts, driving consensus, and working cross-functionally Working at Allica Bank At More ❯

and Infrastructure as Code (IaC) engineering, gained through hands-on development experience. Key areas of proficiency include: Programming Languages: Python, Java and Go. AI/ML: prompt engineering, LLMs, RAG, Semantic Search, Vector Databases, etc Behavior-Driven Development (BDD) Testing: Cucumber, JBehave, Pytest-BDD, etc. CI/CD (Continuous Integration/Continuous Delivery) pipelines : Harness, Tekton, Jenkins, etc. Chaos Engineering More ❯

the universe! Kraken powers some of the most innovative global developments in energy. Were a technology company focused on creating a smart, sustainable energy system. From optimising renewable generation, creating a more intelligent grid and enabling utilities to provide excellent customer experiences, our operating system for energy is transforming the industry around the world in a way that … customer interactions, and real-time operational signals, powering the next generation of intelligent decision support. Stay on the forefront of emerging technologies in LLMs, RAG (Retrieval-Augmented Generation), reinforcement learning, and agentic workflows and translate them into practical, scalable products. What You'll Need Proven leadership experience in managing multi-disciplinary engineering … and platform engineering. Understanding of ML lifecycle from data ingestion and feature engineering to model training, evaluation, and deployment in production. Hands-on experience deploying LLM-based systems (e.g., RAG pipelines, tool calling, fine-tuning, RLHF) and integrating them into real-world applications. Experience developing customer-facing AI tools, voice-based interfaces, or agent augmentation systems is highly desirable. Strong More ❯

likely to extend this is paying £475 a day In order to be considered for this role you will need to have - LLM applications: LangChain, Pydantic-AI (similar frameworks), RAG systems, MCP servers Front end: some experience with Typescript, REACT Application servers, web servers, database servers, docker Some context: frontend (TS/React) and a backend (Python/FastAPI) leveraging More ❯

working with a small, agile team focused on deploying open-source models into production environments. Key Responsibilities Fine-tune and evaluate LLMs for domain-specific tasks Build and optimise RAG pipelines using vector databases Develop prompt engineering strategies and orchestration flows Integrate models into backend services via APIs Implement evaluation frameworks for response quality and reliability Essential Skills Strong Python More ❯

environments like Cursor, Windsurf, Claude Code TDD mindset and comfort with Agile/XP workflows (pairing, code reviews, CI) Nice to Have Exposure to multi-agent systems, memory, or RAG pipelines Develop and maintain agent-facing UIs with React & TypeScript Understanding of agent monitoring, safety, and governance best practices Apply for the job Do you want to join our team More ❯

team that's pushing boundaries in autonomous AI What You'll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical solutions What You'll More ❯

optimized for complex financial workflows Event-driven distributed systems handling sensitive data at enterprise scale The Tech Stack AI/ML : Multi-agent frameworks, fine-tuning, reinforcement learning, advanced RAG Platform : AWS serverless, event-driven architecture, microservices Languages : Python, with modern ML libraries (PyTorch, TensorFlow) What They're Looking For 5+ years developing AI/ML systems beyond basic integration More ❯

high 2:1, preferably in a technical subject Proficiency in Python and its ecosystem Active engagement with the AI space and proactive exploration of new AI technologies (Agents, MCP, RAG, Chain-of-Continuous-Thought, etc. More ❯

in the fastest-moving fields of current AI research, in particular in how to integrate a broad range of structured and unstructured multimodal information into AI systems (e.g. with RAG techniques), and get to immediately apply your results in highly visible Amazon products. If you are deeply familiar with LLMs, natural language processing and machine learning, this may be the More ❯

to leverage these capabilities in 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 out a range of technical More ❯