16 of 16 Retrieval-Augmented Generation Jobs in Bristol

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

improve Design and ship multi-agent systems and production-grade automations that measurably improve how internal teams and customer-facing staff work Build RAG pipelines for internal knowledge retrieval that ground agents in real organisational context Keep the AI tooling stack healthy as usage scales across 2,500+ … development Hands-on experience building multi-agent systems using agentic frameworks — LangChain, LangGraph, MCP, Google ADK or similar Experience with RAG pipelines — retrieval-augmented generation for internal knowledge and workflow automation Background in enterprise tooling — rapid prototyping tools (Vercel/Railway), identity automation (Entra ...

advisor to client CxOs. Key Responsibilities Architecture and Solution Design: Architect enterprise AI solutions using frontier models (Claude, GPT, Gemini family) across agentic automation, RAG, copilots, and multi-modal applications. Pre-Sales and Discovery: Lead technical solutioning during pre-sales and discovery, translating client business challenges into scalable AI architectures … specifically on applied AI/ML or Generative AI. Hands-on GenAI Depth: Proven experience architecting solutions with frontier modelsincluding prompt engineering, fine-tuning, RAG, and agentic/multi-agent system design. LLMOps/MLOps Grounding: Expertise in evaluation frameworks, observability, cost and latency optimization, guardrails, and safe deployment ...

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 ...

leadership across a multidisciplinary AI engineering team Shape the architecture and technical direction for AI across the product suite Lead the delivery of LLM, RAG and agentic AI solutions Take ideas from discovery and proof of concept through to production Establish standards for evaluation, security, observability, governance and cost control … ability to lead others, make architectural decisions and communicate technical trade-offs clearly to non-technical stakeholders. Experience with Azure OpenAI, Azure AI Search, RAG, agentic workflows, Semantic Kernel or Microsoft Agent Framework, vector databases, evaluation frameworks and responsible AI controls would be particularly useful. Salary and benefits ...

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 ...