76 to 100 of 128 Retrieval-Augmented Generation Jobs in the North of England

integrating Generative AI/LLM functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working ...

priorities and deliver results in a fast-paced environment Desirable Experience with generative AI, large language models (e.g. LangChain, vector databases) and retrieval-augmented generation Knowledge of AutoML, hyperparameter tuning and advanced MLflow features Experience with streaming and real-time data platforms Familiarity with ...

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 ...

productive teams Experience leading teams in fast‐growing startups or enterprise software companies Knowledge of financial planning, analytics, or business intelligence domains Experience with RAG systems, vector databases, and semantic search Familiarity with model fine‐tuning, evaluation, and continuous improvement workflows Background in distributed systems, microservices, or cloud‐native architectures ...

capabilities, and appropriate use cases. Strong understanding of AI, ML and LLM concepts, including prompt engineering, prompt grounding, iterative loop techniques, context windows, embeddings, RAG, agentic workflows. Proven ability to integrate AI capabilities both into low-code automation flows and high-code stacks, including, applications, APIs, microservices, distributed systems … code and high‐code platforms. Hands‐on use of AI coding assistants (e.g., GitHub Copilot, Claude Code) and autonomous software engineering agents. Exposure to RAG, vector databases, embeddings, and AI retrieval systems. Experience working with cloud AI services and orchestrating AI agents. Exposure to DevOps practices, CI/ ...

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 ...

work forward independently. What makes you stand out? Experience building AI/LLM-enabled products in production or controlled enterprise environments (e.g. agentic workflows, RAG, prompt engineering, evaluation and governance patterns) Experience with React/Next.js and modern web tooling. Familiarity with document/NoSQL stores (e.g. MongoDB) and data ...

transformation programme. You will design and build scalable data pipelines in GCP, develop cloud-native solutions, and support AI/LLM and RAG initiatives across a large-scale environment. You will work on event-driven architectures with Kafka, write robust Python and SQL, and help ensure data quality, governance ...

transformation programme. You will design and build scalable data pipelines in GCP, develop cloud-native solutions, and support AI/LLM and RAG initiatives across a large-scale environment. You will work on event-driven architectures with Kafka, write robust Python and SQL, and help ensure data quality, governance ...

business environment Deep expertise in Python and modern AI/ML frameworks and SDKs Strong experience building and deploying production‐grade AI solutions, including RAG pipelines and LLM‐based applications Hands‐on experience with AI deployment, operationalisation, monitoring, and scaling Strong understanding of enterprise infrastructure concepts including VNets, Entra ...

across a range of AI and cloud projects. Responsibilities will include: Designing and delivering Generative AI and Machine Learning solutions Building applications using LLMs, RAG frameworks and AI orchestration tools Working directly with clients to understand business challenges and define AI-driven solutions Leading architecture and engineering decisions across Azure … experience building AI-powered applications and deploying them into production Software Engineering or Product Engineering background Strong Python development skills Experience working with LLMs, RAG architectures and modern AI frameworks Multi-cloud experience across Azure, AWS and GCP Strong understanding of cloud-native architectures and scalable application design Experience with ...

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 … GitHub 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 datasets ...

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 ...

applications in production environments. Experience with vision‐language models or multimodal AI over images, video, inspection evidence, diagrams, screenshots, or technical records. Experience with RAG over structured and unstructured data. Familiarity with observability, log analysis, incident response, support tooling, runbooks, or developer tools. Experience building workflow UIs where AI assists ...

Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data. You'll work inside … eventually run these systems without long-term reliance on external suppliers. Key Responsibilities Hands-On Build & Delivery Design, build, and ship production AI components - RAG pipelines, retrieval and embedding infrastructure, orchestration logic, and integration layers - writing high-quality, tested, maintainable code and staying close to implementation rather than ...

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 ...

develop new skills and hone existing ones. We work for a huge variety of interesting, international, and world-leading clients on exciting next generation technology-enabled projects. We are looking for candidates with AI/GenAI Architect based expertise to join us, working in fast-paced teams with … content around data design, architecture and engineering like collateral and playbooks on KX, improving our artefacts and strategies.Knowledge of generative AI architectural patterns including RAG & fine tuning.Experience training & deploying machine learning models using a range of techniques (e.g. Classification, Regression, Neural Networks, etc).Experience with developing and deploying applications ...

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 ...

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 ...

grade deployment. The role: - Design AI and GenAI architecture strategies and roadmaps - Build scalable AI/ML and Agentic AI platforms - Work across LLMs, RAG, embeddings and semantic search - Support AI deployment, governance and integration patterns - Collaborate with engineering, data, DevOps and business teams - Advise clients on AI platform capability … implementation strategy Tech environment: Python LLMs (prompt engineering, fine-tuning, RAG) LangChain/LangGraph Agent frameworks Vector databases MLOps/LLMOps AWS/Azure/GCP/Databricks Kubernetes & GPU infrastructure What you'll bring: - Experience designing enterprise AI or ML architectures - Strong grasp of cloud and modern data platforms ...

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 ...

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 ...

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 ...