1 to 25 of 59 Retrieval-Augmented Generation Jobs in the South East

deploy Generative AI applications, including AI assistants, intelligent search solutions, content automation tools, and Retrieval-Augmented Generation (RAG) platforms. * Build and maintain scalable AI workflows and integrations across internal systems, APIs, and cloud environments. * Develop proof-of-concepts and rapidly iterate solutions, balancing innovation … knowledge of frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent. * Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions, vector databases, and enterprise search capabilities. * Experience integrating AI solutions into existing business systems and workflows. * Strong understanding of cloud platforms, preferably Azure, including ...

Senior AI Engineer (LLMs, RAG & Agentic Systems) | £75,000 - £90,000 | London Hybrid An award-winning and highly innovative organisation is looking to hire a Senior AI Engineer to join a growing AI and Data Science function delivering cutting-edge AI products into production. This is an opportunity to work … heart of the business. You'll be working on modern Generative AI use cases including Retrieval-Augmented Generation (RAG), agentic workflows, conversational AI, recommendation systems and intelligent automation solutions. Working alongside Data Scientists, Engineers and Product teams, you will take ownership of the full ...

Machine Learning solutions into production environments Build and optimise Generative AI applications using Large Language Models (LLMs) Develop Retrieval Augmented Generation (RAG) architectures and AI agents Build speech-to-text, voice AI and accessibility-focused solutions Create intelligent chatbots, virtual assistants and conversational … Learning Commercial experience developing Machine Learning solutions Experience working with Large Language Models (LLMs) Knowledge of Generative AI technologies RAG (Retrieval Augmented Generation) implementation experience NLP and Conversational AI development Model training, testing and evaluation Prompt Engineering and AI workflow optimisation Programming & Frameworks Strong ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop FullStack applications for both GenAI and non-GenAI projects. ...

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 ...

algorithms from scratch. You'll work at the forefront of: AI Assistants Agentic systems Retrieval-Augmented Generation (RAG) Enterprise-grade AI solutions What You'll Be Doing Designing and delivering AI-powered applications using modern cloud platforms Building and operating LLM-based systems , including … RAG pipelines and agents Developing robust, scalable solutions using software engineering best practices Integrating AI capabilities into existing systems and workflows Monitoring performance, cost, reliability, and security of AI services in production Collaborating with cross-functional teams to bring AI ideas to life Ensuring AI systems meet ethical, governance ...

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 ...

deploy Generative AI systems end to end – from use-case discovery through to production Build Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, and enterprise data sources Develop agent-based workflows, orchestration layers, and multi-step reasoning systems Work extensively with open-source … experience building and deploying Generative AI or applied ML systems Strong hands-on engineering background – you actively code, build, test, and deploy Experience with: RAG architectures and vector databases Hugging Face models, pipelines, and inference tooling Prompt engineering and LLM evaluation techniques Agentic systems and orchestration patterns Cloud-native experience ...

Create AI agents, automated workflows, and connectors within Microsoft Copilot Studio and Claude Cowork. Develop Retrieval-Augmented Generation (RAG) pipelines, document automation tools, and LLM-powered solutions. Contribute to Azure infrastructure, including data pipelines, storage, integrations, governance, and security. Collaborate with technical and business ...

implementing AI agents, automation workflows and AI-powered business solutions. Strong understanding of prompt engineering, Retrieval-Augmented Generation (RAG), tool/function calling and LLM integrations. Excellent business analysis, stakeholder management and workshop facilitation skills. Ability to translate complex business requirements into practical, scalable ...

transformation, and retrieval across structured and unstructured data sources. Support the development of retrieval-augmented generation (RAG) frameworks, including embedding strategies, vector search, and data pipelines. Embed data governance, security, privacy, and Responsible AI practices across the knowledge layer. Collaborate with architecture … data architecture, data engineering, or knowledge architecture within enterprise environments. Hands-on experience with AI data foundations, including embeddings, vector databases, and retrieval mechanisms (RAG). Proven track record working on large-scale transformation programmes, ideally within financial services (preferred/strong advantage). Solid understanding of data ...

Azure AI services Proven track record delivering AI prototypes, pilots or innovation projects. Experience with Retrieval-Augmented Generation (RAG), prompt engineering and knowledge-grounded AI solutions. Familiarity with Microsoft 365, SharePoint, Teams and Copilot-related technologies. Understanding of security, governance, data privacy and responsible ...

experience using frameworks such as PyTorch, TensorFlow, LangChain, or similar. * Experience working with LLMs, prompt engineering, vector databases, and retrieval augmented generation (RAG) architectures. * Strong knowledge of cloud platforms including AWS or Azure. * Experience with data engineering and orchestration tools such as Databricks, Airflow ...