26 to 50 of 842 Retrieval-Augmented Generation Jobs in the UK

Machine Learning solutions**.* Drive innovation in **Generative AI, Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), Deep Learning, and Transformer-based architectures**.* Define the technical vision for AI products, ensuring solutions are **scalable, secure, maintainable, and aligned with business objectives … .## TECHNICAL EXPERTISE* Extensive experience designing, building, and deploying **production-grade AI and Machine Learning solutions** at scale.* Deep expertise in **LLMs, Generative AI, RAG, Agentic AI, Deep Learning, Neural Networks, and Transformer architectures**.* Hands-on experience with **LLM evaluation, benchmark design, model validation, prompt engineering, guardrails ...

language) AI coding tools: GitHub Copilot, Cursor or equivalents Prompt engineering and context design patterns Retrieval-augmented generation (RAG), vector search, embeddings (desirable) Evaluation frameworks and automated quality pipelines for AI outputs Technical and Professional Expertise Solid production experience with C# .NET, including working … results to improve quality. Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents. Preferred Qualifications And Experience Retrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings. Experience with LLM APIs across more than one provider ...

Machine Learning solutions for customer-specific use cases. Build AI assistants, copilots, intelligent automation, and retrieval-augmented generation (RAG) applications. Design and integrate AI workflows with enterprise systems, databases, APIs, and business applications. Fine-tune prompts, optimize AI workflows, and improve model performance. Deploy … regulated industries such as Healthcare, Financial Services, Insurance, Telecom, or Manufacturing. Knowledge of AI governance, responsible AI, security, and compliance frameworks. Experience implementing RAG architectures and enterprise AI assistants. Exposure to AI observability, evaluation frameworks, guardrails, and monitoring platforms. Experience with Agentic AI and Multi-Agent Systems. Relevant certifications ...

language) AI coding tools: GitHub Copilot, Cursor or equivalents Prompt engineering and context design patterns Retrieval-augmented generation (RAG), vector search, embeddings (desirable) Evaluation frameworks and automated quality pipelines for AI outputs Technical and Professional Expertise Solid production experience with C# .NET, including working … results to improve quality. Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents. Preferred Qualifications and Experience Retrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings. Experience with LLM APIs across more than one provider ...

Greater London - United Kingdom Type: Permanent Senior AI Engineer - London (Remote) We're looking for an experienced AI Engineers to lead the next generation of AI-powered learning experiences. You'll play a pivotal role in defining our AI strategy, architecting scalable solutions, and bringing innovative products … products in production. Expert-level software engineering skills, particularly in Python. Strong experience with LLMs, Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, and model evaluation. Experience building cloud-native applications on Azure, AWS, or Google Cloud. Strong understanding of MLOps, CI/ ...

field. Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering best practices ...

field. Demonstrable experience of deploying modern AI/ML solutions into production,including prompt engineering, retrieval-augmented generation (RAG),modelevaluation,andmonitoringusing metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering best practices(CI/ ...

experience developing applications using Large Language Models (LLMs), including prompt engineering, structured outputs, tool calling, retrieval-augmented generation (RAG) and evaluation techniques. Experience with modern LLM and agentic AI frameworks such as LangChain, LangGraph and Microsoft Foundry or equivalent technologies. Strong understanding of software ...

policy summarisation, or intelligent routing. Build or consume vector-search capabilities (e.g., OpenSearch, Pinecone) for retrieval-augmented generation (RAG) patterns. Use AI coding assistants (Claude Code, GitHub Copilot, Cursor, etc.) fluently as part of your daily development workflow to raise throughput and quality. Contribute … Azure DevOps, GitHub Actions, or equivalent). Demonstrated hands-on exposure to at least one of: LLM API integration, vector databases, prompt engineering, or RAG pipeline construction — even in a personal project or hackathon context. Familiarity with REST-based AI APIs (OpenAI, Anthropic, Google Vertex AI, or equivalent). Basic ...

field. Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering best practices ...

integrating AI-powered applications and services. Familiarity with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), AI agents, or machine learning workflows. Experience integrating AI APIs and platforms such as OpenAI, Anthropic, Azure AI, or similar technologies. Knowledge of vector databases, embeddings ...

/CD pipelines and MLOps practices Drive technical quality, performance and operational resilience Design and implement Large Language Model solutions and Retrieval-Augmented Generation architectures Implement prompt engineering and AI orchestration frameworks Evaluate AI model performance, accuracy and cost optimisation Use Microsoft Copilot, Azure … concepts Knowledge of MLOps, DevOps and CI/CD practices Hands‐on experience with Large Language Models (LLMs), prompt engineering, Retrieval‐Augmented Generation (RAG), vector databases, and AI orchestration frameworks such as LangChain or Semantic Kernel Understanding of model evaluation methodologies Knowledge of responsible ...

language models across text, image, audio, and videoDesign and implement agentic workflows using prompt engineering, Retrieval-Augmented Generation (RAG), APIs, and enterprise data integrationsFine-tune, optimize, and deploy large language and multi-modal models with a focus on scalability, reliability, and low-latency performanceBuild … problem-solving skills within agile delivery environmentsBonus Points ForExperience building AI agent frameworks or autonomous orchestration platformsKnowledge of vector databases, semantic search, and advanced RAG architecturesExperience with cloud platforms such as AWS, Azure, or GCP for AI workloadsFamiliarity with financial services, banking, or regulated enterprise environmentsExposure to AI governance, model ...

ClinicalKey AI, PharmaPendium, and next-generation life sciences platforms. These products leverage retrieval-augmented generation (RAG), semantic search, and generative AI to make knowledge more discoverable, connected, and actionable across disciplines. The Search & AI Evaluation team sits within the Platform Data Science … systems. You will own complex problem areas end-to-end, drive methodological rigor in evaluation, and contribute to the technical direction of retrieval and RAG systems. This role is ideal for someone with deep hands-on experience in search/retrieval systems, RAG pipelines, and evaluation ...

experience building AI agents using frameworks such as LangGraph, LangChain, or LlamaIndex. Experience implementing Retrieval-Augmented Generation (RAG) solutions and governance controls for enterprise AI applications. Experience using agentic AI to automate engineering workflows such as testing, code reviews, documentation, operational support, and data ...

tools such as LangChain, LlamaIndex, Hugging Face, and APIs from OpenAI and Anthropic. Building retrieval-augmented generation (RAG) prototypes with vector stores and knowledge graphs. Developing and testing agentic architectures through our own Genie Platform – enabling AI agents that reason, retrieve, and act across … language suited to AI system development and data workflows. Experience integrating models with APIs, data sources, or production systems. Curiosity about LLMs, RAG pipelines (Graph & Vector based), and agent frameworks. Understanding of cloud-native (AWS/GCP/Azure) and DevOps/DevSecOps practices. A collaborative mindset and willingness ...

tools such as LangChain, LlamaIndex, Hugging Face, and APIs from OpenAI and Anthropic. Building retrieval-augmented generation (RAG) prototypes with vector stores and knowledge graphs. Developing and testing agentic architectures through our own Genie Platform – enabling AI agents that reason, retrieve, and act across … language suited to AI system development and data workflows. Experience integrating models with APIs, data sources, or production systems. Curiosity about LLMs, RAG pipelines (Graph & Vector based), and agent frameworks. Understanding of cloud-native (AWS/GCP/Azure) and DevOps/DevSecOps practices. A collaborative mindset and willingness ...

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 … 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 ...

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 … 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 ...

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 … 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 ...

pipelines, distributed training frameworks, inference/serving platforms, MLOps/LLMOps tooling, vector databases, and retrieval-augmented generation (RAG) and agentic architectures. Define and AI governance frameworks covering model risk management, responsible AI, data lineage, bias/fairness testing, explainability, and regulatory alignment … LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI).Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic frameworks (LangChain, LangGraph, Semantic Kernel).Data pipelines: data lake/lakehouse architectures, ETL/ ...

hands-on AI & Data Engineer to design, build and productionise AI agents, automation solutions, Retrieval-Augmented Generation (RAG) capabilities and data products supporting Balance Sheet Modelling . The role will work closely with business, architecture and technology teams to translate business use cases into … Umbrella. Remote First Working - Travel to local site when required. Key Responsibilities Design, develop and deploy production-ready AI agents and agentic workflows. Build RAG solutions, integrating Large Language Models (LLMs) with enterprise data and knowledge sources. Apply prompt engineering techniques to improve the accuracy, reliability and effectiveness ...

Data Version Control Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation (RAG) Familiarity with LLMOps frameworks such as LangChain, LangSmith or LangGraph Understanding of model validation, evaluation techniques and production monitoring Experience working in cross-functional teams from ...

effectively as part of their day-to-day workflow. Bonus points for: Experience with retrieval-augmented generation (RAG) and vector databases (pgvector, Pinecone, or similar).Experience designing multi-agent systems and agent orchestration patterns. Familiarity with LLM observability tools (LangSmith, Langfuse, Arize, or similar ...

deployment of GenAI solutions, including LLM-based applications, prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG) for enterprise use cases. The ideal candidate has a strong foundation in machine learning and NLP, with hands‐on experience in modern GenAI tools and frameworks ...