26 to 50 of 128 Retrieval-Augmented Generation Jobs in the North of England

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 - Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 - AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 - Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 - Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

across the organisation. Build AI assistants, copilots, intelligent automation tools and knowledge management solutions. Develop Retrieval-Augmented Generation (RAG) solutions using internal business data. Create scalable AI architectures and support the ongoing management of AI platforms and infrastructure. Integrate AI solutions with business-critical … experience building and deploying AI or Machine Learning solutions. Strong Python development skills. Experience working with REST APIs and systems integration. Knowledge of LLMs, RAG architectures, vector databases and modern AI frameworks. Experience with AWS cloud services and infrastructure. Familiarity with Docker and Kubernetes. Experience integrating AI solutions with Microsoft ...

product management.Build and integrate AI-powered features into web applications and enterprise tools.Build and optimize Retrieval-Augmented Generation (RAG) solutions to power chatbot interactionsWork both independently and paired with other team members to ensure efficient delivery of prioritised features.Take part in the planning … programming.Experience using modern AI-assisted and agentic development workflows in day-to-day engineering using AI/ML APIs and SDKsHands-on experience developing RAG solutions or working with LLMs for chatbots is preferredIn-depth understanding of CI/CD and the tools used to implement them, such as Jenkins.Experience ...

build analytical tools, AI-assisted insight products, and intelligent automation — including prompt engineering and retrieval-augmented generation (RAG). Validate all AI and model outputs for accuracy, bias, and reliability before they reach stakeholders.Translate reporting and analytical findings into clear, business-ready insight … data analysis, modelling, and scripting.Machine learning — knowledge in building and applying supervised and unsupervised models to real business problems.Generative AI and LLMs — prompt engineering, RAG, and applied use of LLM tooling in an analytical context.Automation tooling — experience with platforms such as Power Platform, Power Automate, or equivalent.Communication, business requirements ...

experience of this would be really useful. Building agentic AI solutions, LLM-powered applications and retrieval-augmented generation (RAG) architectures. Experience with Vertex AI Agent Builder, Agentspace, LangGraph or multi-agent frameworks. Knowledge of responsible AI, model governance and risk management practices within regulated ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Build and integrate AI-powered features into web applications and enterprise tools. Build and optimize Retrieval-Augmented Generation (RAG) solutions to power chatbot interactions Work both independently and paired with other team members to ensure efficient delivery of prioritised features. Take part … Experience using modern AI-assisted and agentic development workflows in day-to-day engineering usingAI/ML APIs and SDKs Hands-on experience developing RAG solutions or working with LLMs for chatbots is preferred In-depth understanding of CI/CD and the tools used to implement them, such ...

both customers and organisations. Modern AI architecture expertise. Deep understanding of machine learning, LLMs, retrieval-augmented generation (RAG), agentic systems and the architectural patterns used to deploy AI solutions at enterprise scale. Engineering and cloud foundations. Strong understanding of software engineering, data architecture ...

value for both customers and organisations.Modern AI architecture expertise. Deep understanding of machine learning, LLMs, retrieval-augmented generation (RAG), agentic systems and the architectural patterns used to deploy AI solutions at enterprise scale.Engineering and cloud foundations. Strong understanding of software engineering, data architecture ...

help build the data foundations that power enterprise search, knowledge discovery, AI applications and Retrieval-Augmented Generation (RAG). What you'll be doing Design and build scalable ETL/ELT data pipelines across multiple enterprise data sources Develop data solutions using BigQuery, Google ...

products and wider AI strategy.You'll design, build and operate the cloud-native platform supporting Retrieval-Augmented Generation (RAG), LLM orchestration, vector search and production AI services, working closely with AI Engineering, DevOps and Security teams to deliver scalable, secure and reliable AI solutions. … Role scope: Designing, building and operating AI platform infrastructure across Microsoft Azure. Developing and optimising RAG pipelines, AI services and platform components. Deploying infrastructure using Terraform and supporting containerised applications. Building and maintaining CI/CD pipelines using GitHub and Azure DevOps. Improving observability, monitoring and platform resilience. Supporting vector ...

within SAP consulting, working with the latest AI agent frameworks, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines to help clients unlock new value, streamline complex processes, and accelerate their digital transformation journeys. This is more than a delivery role. … prioritised, and your career can accelerate quickly. What makes this role different? A specialist innovation focused on SAP consulting, working with AI agents, LLMs, RAG pipelines, and agentic workflows rather than traditional SAP configuration. Exposure to high-profile, complex transformation programmes across multiple industries, delivering cutting-edge technology solutions alongside ...

Integrate AI/ML components into applications and operational workflows Design and implement Retrieval-Augmented Generation (RAG) solutions Evaluate LLM performance, identify failure modes, and implement mitigation strategies Test, debug, and improve non-deterministic AI systems Balance AI approaches with deterministic or statistical methods … performance Ability to debug complex, non-deterministic behaviours Strong communication skills to explain AI trade-offs and risks Bonus Skills Hands-on experience with RAG architectures and LLM tooling Experience working in regulated or high-assurance environments Familiarity with AI safety, risk, and governance considerations Background in software engineering (Python ...

fundamentals. Experience with one or more of the following: Agentic AI Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Edge AI Multi-modal AI AI explainability AI safety, robustness or security Emerging machine learning techniques A strong appreciation for software engineering best practices, including ...

coverageYour qualificationsEarly-career professional with proven commercial experience in:Troubleshooting Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) applicationsRobust prompt and context engineeringData analysis (Excel, Anaplan or similar tools)Software development or quality assuranceLevel 2 or level 3 technical supportA degree in Mathematics ...