19 of 19 Retrieval-Augmented Generation Jobs in Bristol

ready AI solutions using modern Azure technologies and cloud-native architecture.Integrate Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and Agentic AI capabilities into business applications.Develop responsive frontend applications alongside robust APIs and backend services to create seamless user experiences.Work closely with Product, Business … Services and cloud-native application development.Experience implementing AI solutions including LLM integration, prompt engineering, Retrieval-Augmented Generation (RAG) and vector databases.Experience working with SQL and NoSQL databases, with knowledge of platforms such as SQL Server, Snowflake, OneLake or similar data platforms.Strong understanding of software ...

solutions using modern Azure technologies and cloud-native architecture. Integrate Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and Agentic AI capabilities into business applications. Develop responsive frontend applications alongside robust APIs and backend services to create seamless user experiences. Work closely with Product … Services and cloud-native application development. Experience implementing AI solutions including LLM integration, prompt engineering, Retrieval-Augmented Generation (RAG) and vector databases. Experience working with SQL and NoSQL databases, with knowledge of platforms such as SQL Server, Snowflake, OneLake or similar data platforms. Strong ...

Azure environment.Define technical standards and best practices for developing advanced Generative AI applications, encompassing complex Retrieval-Augmented Generation (RAG) architectures, prompt and context engineering, and Large Language Model (LLM) integration strategies.Architect innovative Agentic AI systems, establishing frameworks for orchestration, memory management, and robust evaluation.Shape … within Azure environments, with a focus on technical escalation and resolution.Expertise in establishing technical standards and best practices for advanced Generative AI applications, encompassing RAG architectures, prompt engineering, and LLM integration strategies.Experience in architecting and developing Agentic AI systems, including frameworks for orchestration, memory management, and performance evaluation.Advanced knowledge ...

develop, test and deploy LLM and agentic AI solutions Contribute to the development of LLM, Retrieval-Augmented Generation (RAG) and agentic AI solutions, including evaluation, optimisation and monitoring.Implement reliable and scalable AI solutions using established software engineering, MLOps and LLMOps practices.Contribute across … Proven experience engineering technology solutions in complex environments and supporting production operations Applied AI/ML and GenAI experience with knowledge of established patterns (RAG, tool use, guardrails, evaluation, agents) and an ability to evaluate tools and approaches Experience working with conversational AI platforms and customer-facing AI applications. Experience ...

understanding of generative AI and the modern AI stack, including large language models, prompt engineering, retrieval-augmented generation (RAG), vector databases, evaluation/monitoring and MLOps principles, with the ability to shape investment cases and operating models at scale.Extensive experience providing executive oversight ...

Collaborate with multidisciplinary teams to develop Generative AI proof-of-concepts using technologies such as Retrieval-Augmented Generation (RAG) and intelligent agents. Scale successful proof-of-concepts into robust, production-ready solutions. Design and develop full stack applications across both AI-driven and traditional … success. You will work on impactful projects, contribute to the adoption of emerging technologies and play a key role in delivering next-generation AI solutions. Reasonable Adjustments: Respect and equality are core values to us. We are proud of the diverse and inclusive community we have built ...

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 ...

ambiguous environments and owning projects from concept to deployment. Nice to Have Experience with Retrieval-Augmented Generation (RAG). Knowledge of vector databases and semantic search. Experience with Python alongside TypeScript. Familiarity with AI orchestration frameworks such as LangChain, LangGraph, or similar. Experience with … customers. Technologies You'll Work With Generative AI Artificial Intelligence (AI) AWS TypeScript Large Language Models (LLMs) Rapid Prototyping APIs Data Engineering Vector Databases RAG React/Next.js Node.js Git Docker What Success Looks Like Deliver AI solutions that solve real customer problems. Build production-ready applications in weeks ...

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 ...

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 ...

custom orchestration frameworks . Integrate leading and self-hosted LLMs with tools, data and external systems using MCP and custom connectors . Implement RAG solutions using vector databases and hybrid retrieval where appropriate. Work across relational, document, key-value and graph databases depending on the problem. Develop prompt ...

libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps and production AI practices, including CI/CD, model versioning ...

PyTorch, TensorFlow, scikit-learn) and practical implementation of model development and deployment.Working knowledge of Generative AI, including large language models, prompt engineering, retrieval-augmented generation and evaluation approaches.Experience with MLOps and modern engineering practices, including CI/CD pipelines, model versioning, monitoring and automated ...

Amazon Comprehend, Amazon Transcribe, Amazon Rekognition and related managed AI services.· Generative AI solution design, including foundation model selection, prompt engineering, retrieval-augmented generation, model evaluation, responsible AI controls and enterprise integration patterns.· Data and AI platform architecture on AWS, including data ingestion, governance ...

agentic AI solutions. Strong understanding of Azure AI architecture, security, governance and best practice. Experience connecting internal/proprietary business data to LLMs, including RAG, grounding, model customisation and/or fine-tuning. The ability to operate as both a hands-on technical SME and trusted advisor. Previous experience training ...

Hugging Face), advanced GenAI frameworks (LangChain, HF Transformers, LlamaIndex) & Agentic architectures (LangGraph, SmolAgents, PydanticAI) Prompt Engineering: Deep prompt engineering & optimization skills for GenAI.Vector DBs & RAG: Designed solutions using vector DBs & Retrieval Augmented GenAI (RAG) for knowledge applications.GenAI ModelOps/MLOps & Governance: GenAI ModelOps/MLOps knowledge … with ethical AI governance focus.Multimodal & Code Generation: Architected GenAI for multimodal (vision, audio, language) & code generation.ER&I GenAI Applications: Applied GenAI to ER&I use cases to create business value.Enterprise Software Integration: Designed GenAI integrations with SaaS/ERP for business process automation.GenAI Impact Reporting: Designed advanced reporting ...

Integrate agents and applications with existing client platforms using Microsoft Graph, REST APIs and Azure services Design solutions involving knowledge retrieval, RAG, document processing and autonomous workflows Advise clients on AI security, governance, identity and deployment considerations Contribute to technical documentation, development standards and solution architecture Work across … Platform , particularly Power Automate and Power Apps Microsoft 365 Copilot and the wider M365 ecosystem Microsoft Foundry/Azure AI services Azure AI Search, RAG and generative AI solutions Conversational AI, agentic AI and autonomous workflows Microsoft Graph and REST API integrations Dataverse, SharePoint and Teams Entra ID, app registrations ...

products, architecture and future AI roadmap. What you'll do Build AI-powered features within existing C#/.NET products Develop LLM solutions using RAG, agentic workflows and Azure AI Take ideas from proof-of-concept through to production Help shape technical decisions and the wider AI strategy Improve … moved beyond experimenting with AI and can demonstrate something you've genuinely delivered into production. Experience with Azure OpenAI, Azure AI Search, RAG, Semantic Kernel, vector databases or Python-based AI tooling would be particularly valuable. What's in it for you? Up to £70,000 plus bonus Mostly remote ...