101 to 125 of 125 Retrieval-Augmented Generation Jobs in the North of England

language model APIs across multiple providersAI coding tools: GitHub Copilot, Cursor or equivalentsPrompt engineering and context design patternsRetrieval-augmented generation (RAG), vector search, embeddings (desirable)Evaluation frameworks and automated quality pipelines for AI outputsTechnical and Professional ExpertiseSolid production experience with C# .NET, including working in established … improve quality. Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents. Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings. Experience with LLM APIs across more than one provider, with a feel for their ...

language model APIs across multiple providersAI coding tools: GitHub Copilot, Cursor or equivalentsPrompt engineering and context design patternsRetrieval-augmented generation (RAG), vector search, embeddings (desirable)Evaluation frameworks and automated quality pipelines for AI outputsTechnical and Professional ExpertiseSolid production experience with C# .NET, including working in established … improve quality. Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents. Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings. Experience with LLM APIs across more than one provider, with a feel for their ...

company's internal AI platform and turn successful approaches into reusable tools and accelerators. What you'll be working on: Building AI solutions using RAG, AI agents, LLM integrations and MCP Taking solutions from prototype through to production, including security, scalability, guardrails and observability Building AI evaluation and testing frameworks … languages Full-stack capability, including back-end and data engineering Proven experience building and shipping production LLM/AI applications Hands-on experience with RAG, AI agents, prompt engineering, tool orchestration and MCP Experience with AI evaluation, testing, datasets and guardrails Client-facing experience and an understanding of enterprise security ...

build and deploy enterprise-grade AI solutions across an Enterprise Knowledge Base (EKB). This is a hands-on role focused on GenAI, LLMs, RAG and Agentic AI, taking solutions from prototype through to production. You'll work across AI, data and engineering teams to build intelligent retrieval, knowledge discovery and automation capabilities. Key Responsibilities Build production-grade GenAI, LLM, RAG and Agentic AI solutions Develop AI Agents and Multi-Agent Systems Work with LangChain, LangGraph and MCP Build intelligent retrieval using Embeddings, Vector Databases and Semantic Search Apply Prompt & Context Engineering Integrate ...

Developing AI solutions that can reason, retrieve information, make decisions and take actions Working with business data to develop intelligent applications and automation Developing RAG, vector search, embeddings and knowledge-based AI solutions Evaluating new LLMs, AI frameworks and emerging technologies Taking AI concepts from initial idea through to production … following: Agentic AI/AI Agents LLM application development OpenAI, Azure OpenAI, Anthropic, Gemini or similar LLM orchestration and tool/function calling RAG and retrieval-based AI Vector databases and embeddings AI workflow automation Python, C# and/or TypeScript APIs and system integration SQL/databases ...

Designing APIs, services and integrations to connect AI capabilities with wider enterprise systems. Building agentic AI applications, including multi-agent workflows and orchestration. Developing RAG solutions using vector search, enterprise data and retrieval techniques. Writing clean, maintainable and well-tested production code. Working with CI/CD pipelines … building AI-enabled applications using technologies such as Azure AI, Microsoft AI Foundry, Azure OpenAI, Semantic Kernel, Agent Framework or similar. Understanding of RAG, LLMs, vector search and agentic AI concepts. Experience integrating applications with APIs, cloud services and enterprise systems. Strong understanding of DevOps, CI/CD, source control ...

with strong AI engineering and MLOps, and deliver production-grade LLM features (NLQ over telemetry, summarisation, incident triage, on-call copilots) using prompt engineering, RAG, evaluation, and guardrails; collaborate with DS/ML teams on model versioning, monitoring, retraining, and drift detection, including low-latency inference in streaming/serving … dashboardsApplied AI/ML engineering in production – building or integrating AI capabilities (e.g., anomaly detection, forecasting, features) including familiarity with LLM APIs, prompting/RAG/embeddings/vector DBs, and the model lifecycle/MLOps basicsFrontend engineering background JavaScript/TypeScript with React/Angular/Vue, building performant ...

including API design, automated testing, asynchronous processing, dependency management and production debugging. Practical experience with agent orchestration, structured tool calling, workflow state, retrieval-augmented generation and human-in-the-loop patterns. Experience building secure integrations with enterprise APIs, repositories, cloud services, CI/ ...

enable more reliable and scalable product development Establish end-to-end technical architectures for LLM-enabled systems, covering data pipelines, retrieval/RAG, prompt orchestration and agent workflows to support robust and effective AI implementation Analyse complex constraints across product, data, security, legal and operations, translating them into … services and the supporting platform services required for production delivery AI engineering experience delivering LLM-enabled systems using LLM APIs/Bedrock, including RAG architectures, vector databases and orchestration frameworks DevOps skills including CI/CD pipelines, containerisation and monitoring/observability, with experience defining release and operating practices ...

enable more reliable and scalable product development Establish end-to-end technical architectures for LLM-enabled systems, covering data pipelines, retrieval/RAG, prompt orchestration and agent workflows to support robust and effective AI implementationAnalyse complex constraints across product, data, security, legal and operations, translating them into clear … services and the supporting platform services required for production delivery AI engineering experience delivering LLM-enabled systems using LLM APIs/Bedrock, including RAG architectures, vector databases and orchestration frameworks DevOps skills including CI/CD pipelines, containerisation and monitoring/observability, with experience defining release and operating practices ...

date with the rapidly evolving technology landscape.Responsibilities:Develop and integrate AI-powered applications using modern Microsoft and Azure technologies Build and improve RAG-based solutions, intelligent search and knowledge servicesExplore and develop AI agents capable of completing tasks and automating business processesCreate prototypes and proof of concepts to assess … machine learning or generative AI technologiesExperience working with Azure and cloud-based technologiesKnowledge of technologies such as Azure OpenAI, Microsoft Foundry, Semantic Kernel, RAG or vector databasesUnderstanding of AI agents, LLMs and modern AI development approachesStrong software engineering fundamentals, including source control, testing and CI/CDGenuine interest ...

platform that underpins our clinets conversational assistant and wider AI initiatives. You will take responsibility for the runtime, infrastructure, and operational foundations underpinning our RAG pipelines, LLM orchestration, and vector search, built across Microsoft Azure and Microsoft Foundry. Where prototypes and proof-of-concepts exist, you will … Build and extend a scalable AI platform via Microsoft Foundry. Own and optimise Foundry solutions for accuracy, scale, and latency. Harden the production RAG pipeline, covering inference, retrieval, and ingestion. Architect Azure services using Terraform infrastructure-as-code. Scale vector database and embedding pipelines. Build ...

STEM field. Strong leader, able to communicate clearly at all levels. Track record deploying business critical ML models in production. Experience building and scaling RAG applications with semantic search and text embeddings. Cloud computing experience, ideally Azure, AWS, or GCP. Strong ML and data science fundamentals, including NLP. Ability … manage AI/RAG/GenAI risk and fine-tune models on proprietary datasets. Strong Python and SQL skills, with frameworks such as PyTorch and TensorFlow. If you're ready to shape AI strategy from the ground up and see it through to production, at real scale, this ...

concept through to production deployment Designing and developing AI and Machine Learning solutions using Azure and modern AI technologies Building production-ready LLM applications, RAG pipelines and AI agents Working closely with Data Engineering teams to prepare and integrate structured and unstructured data Collaborating with Infrastructure teams to deploy secure … Science and Machine Learning Experience designing and deploying production AI solutions Strong knowledge of AI, Machine Learning and Large Language Models (LLMs) Experience developing RAG solutions and AI agents Excellent Python skills alongside modern AI/ML frameworks and SDKs Experience with Azure AI services or another enterprise AI cloud ...

Loop and MLOps.Data Architecture Design: You understand and know when to use the following data architecture design patterns RAG (Retrieval-Augmented-Generation), Serverless, DWH models, Data Mesh and Data Products.Hands on mindset: You speak the language of data fluently, whether it's Python ...

Neo4j/Knowledge Graph experience Experience with Vector Databases, Search or Indexing Strong understanding of data modelling and governance Experience supporting AI, LLM or RAG use cases Nice to have: Enterprise Knowledge Management/Knowledge Base experience Semantic Search Large-scale Search platforms AI/LLM and RAG experience Financial ...

workflows in Copilot Studio Build scalable components: prompt orchestration, retrieval layers, Power Automate flows, model interfaces, validation pipelines Develop and optimise RAG components - embeddings, vector queries, metadata strategies for accuracy and reliability Integrate AI agents with enterprise systems via Microsoft Graph, APIs, and Power Platform connectors Implement secure … support the satellite model across business functions Requirements Hands-on experience building solutions with LLMs, AIAPIs, Copilot Studio, or agent frameworks Strong understanding of RAG architectures, vector databases, embeddings, and retrieval optimisation Experience with Microsoft Azure AI services, cloud-native engineering, and secure deployment patterns Experience with agent ...

Searchability seeks an experienced AI Engineer to join a major enterprise AI transformation programme, focusing on AI Agents, RAG and GenAI. You’ll design and build agentic AI solutions, and develop APIs in a complex enterprise environment. The role involves working with large language models, prompt engineering, and production-grade ...

Searchability seeks an experienced AI Engineer to join a major enterprise AI transformation programme, focusing on AI Agents, RAG and GenAI. You’ll design and build agentic AI solutions, and develop APIs in a complex enterprise environment. The role involves working with large language models, prompt engineering, and production-grade ...

team of engineers. Key Responsibilities * Lead the development and deployment of AI-powered tools to improve efficiency and productivity * Design advanced solutions including automation, RAG, and LLM-based workflows * Collaborate with stakeholders to identify and deliver strategic AI initiatives that deliver measurable business value * Provide guidance on AI architecture, tooling … with AI engineering, or software engineering (e.g. Python, Java, JavaScript/TypeScript etc.) with libraries/frameworks such as PyTorch, TensorFlow etc.) * Experience with RAG and LLM-based solutions * Solid understanding of AI architectures and data pipelines * Knowledge of security, compliance, and governance principles from a development perspective * Proven mentoring ...

attention to detail. Ability to work effectively within Agile squads and cross-functional environments. Desirable: Exposure to AI testing methods (e.g., validating LLM outputs, RAG consistency, AI behaviour evaluation). Experience using AI-powered QA tools or test-generation frameworks. Knowledge of cloud environments (AWS/Azure ...

pitches and proposals. Technical Delivery & Innovation Familiarity and experience with AI concepts and techniques, such as large language models, machine learning, or Retrieval-Augmented Generation models. Experience or desire to grow capability in using AI to support insight workflow optimisation or automation, looking ...

will build scalable components, integrate with enterprise systems, and implement secure AI practices within a modern cloud-native stack. The role focuses on RAG architectures, vector databases, embeddings, and retrieval optimization, with emphasis on observability, governance, and reusable code assets. Collaborative, cross-functional work is expected. #J ...

strategy, roadmap and architecture. Design secure, scalable and reusable AI solutions and implementation patterns. Identify opportunities to apply Generative AI, LLMs, AI agents, RAG and automation. Develop new AI-enabled products and services, taking solutions from pilot through to production. Create reusable architectures, frameworks, documentation and implementation guidance. Drive internal … responsible AI practices. Key Skills & Experience The successful candidate will have strong experience across: AI & Generative AI: LLMs, foundation models, AI assistants, agents, RAG, enterprise search and workflow automation. Solution & Platform Architecture: Designing scalable, resilient and supportable enterprise solutions. Data & Integration: Data architecture, governance, structured/unstructured data, APIs, search ...

strategy, roadmap and architecture. Design secure, scalable and reusable AI solutions and implementation patterns. Identify opportunities to apply Generative AI, LLMs, AI agents, RAG and automation. Develop new AI-enabled products and services, taking solutions from pilot through to production. Create reusable architectures, frameworks, documentation and implementation guidance. Drive internal … responsible AI practices. Key Skills & Experience The successful candidate will have strong experience across: AI & Generative AI: LLMs, foundation models, AI assistants, agents, RAG, enterprise search and workflow automation. Solution & Platform Architecture: Designing scalable, resilient and supportable enterprise solutions. Data & Integration: Data architecture, governance, structured/unstructured data, APIs, search ...