26 to 45 of 45 Retrieval-Augmented Generation Jobs in Yorkshire

equivalentsPrompt engineering, context design and agentic workflow frameworksEvaluation harnesses, evaluation datasets and automated test suites for AI outputsRetrieval-augmented generation (RAG), vector search, embeddings and retrieval pipelines (desirable)LLM orchestration frameworks (LangChain, Semantic Kernel or equivalents – desirable)Technical and Professional ExpertiseStrong software engineering background … Copilot, Cursor or equivalents, with a practical view of where they help and where they mislead. Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use or fine-tuning in production settings. Experience with LLM APIs, orchestration frameworks, vector search, embeddings, retrieval pipelines, evaluation ...

Lead the implementation of AI-enabled applications and platform capabilities. Establish reusable patterns, frameworks, and best practices for AI engineering. Design and oversee LLM, RAG, and Agentic AI integrations within enterprise environments. Collaborate with architects and engineering teams to ensure scalable, secure, and maintainable solutions. Define standards for AI observability … applications on Azure, AWS, or GCP. Strong knowledge of Kubernetes, Microservices, APIs, Event-Driven Architecture, and DevOps practices. Hands-on experience with LLM integration, RAG architectures, AI orchestration frameworks, and Agentic AI solutions. Experience with AI frameworks and tools such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent. Strong ...

rather than hype to determine whether an AI approach is actually working. It would be advantageous if you have experience with: RAG (Retrieval-Augmented Generation) Agentic workflows and tool use Fine-tuning Vector search and embeddings Retrieval pipelines LLM orchestration frameworks such ...

Lead the implementation of AI-enabled applications and platform capabilities. Establish reusable patterns, frameworks, and best practices for AI engineering. Design and oversee LLM, RAG, and Agentic AI integrations within enterprise environments. Collaborate with architects and engineering teams to ensure scalable, secure, and maintainable solutions. Define standards for AI observability … applications on Azure, AWS, or GCP. Strong knowledge of Kubernetes, Microservices, APIs, Event‐Driven Architecture, and DevOps practices. Hands‐on experience with LLM integration, RAG architectures, AI orchestration frameworks, and Agentic AI solutions. Experience with AI frameworks and tools such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent. #J ...

articulate the business problem behind the technology you're building. It would be advantageous if you also have experience with: RAG (Retrieval-Augmented Generation) Agentic workflows and tool use Vector databases/search and embeddings LLM APIs from multiple providers AI evaluation frameworks ...

DevOps best practices. Collaborate with architects, product teams, and engineers to deliver high-quality solutions. Contribute to AI-enabled application development, including LLM and RAG integrations where applicable. Required Skills 10+ years of software engineering experience. Strong hands-on expertise in Java and/or Python. Kubernetes, Docker, Microservices … REST APIs. CI/CD, DevOps, and Agile delivery practices. Preferred Skills Experience with LLM integration, RAG, AI orchestration, or Agentic AI frameworks. Exposure to AI platforms such as Azure OpenAI, OpenAI, LangChain, LangGraph, or Semantic Kernel. Candidate Profile Strong engineering and problem-solving skills. Product engineering mindset with ...

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 … results to 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 trade ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

organisation’s AI 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 AI adoption … market trends and responsible AI practices.Key Skills & ExperienceThe 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 ...

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 ...