51 to 75 of 961 Retrieval-Augmented Generation Jobs in England

defined metrics. This role requires strong technical expertise across Generative and Agentic AI—including LLMs, retrieval-augmented generation (RAG), autonomous and multi-agent systems, and modern interoperability standards such as the Model Context Protocol (MCP)—coupled with excellent communication skills to engage with clients … respected and valued.Required•Experience of solutioning in Gen AI, Agentic AI, classic ML and automation space•Experience and good understanding of GenAI prompt engineering, RAG pipelines, Supervised/unsupervised ML and AI observability•Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases ...

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 ...

development and deployment of both traditional and GenAI-based machine learning models, including discovery analytics, experimental design, model development, benchmarking, enhancement and deployment.LLM & RAG Integration: Design and implement new Retrieval-Augmented Generation (RAG) pipelines and enhance existing frameworks, applying advanced techniques in data chunking … data engineering, platform engineering) to translate business requirements into impactful AI solutions.Research & Innovation: Stay abreast of emerging tools, techniques, and best practices in LLMs, RAG, GenAI, model development & evaluation techniques and proactively apply new knowledge to drive innovation.About YouQualificationsAdvanced degree (MSc or PhD) in Machine Learning, Natural Language Processing, Artificial ...

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 ...

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 ...

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 ...

machine learning and AI use cases. Understanding of modern GenAI architecture patterns including vector databases, retrieval-augmented generation (RAG), embeddings, agentic workflows and LLM orchestration frameworks. Knowledge of MLOps, model lifecycle management, observability and AI governance practices. Experience with cloud platforms such ...

London, Greater London – United Kingdom Type: PermanentSenior 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 … machine learning 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/CD, Kubernetes, containerisation ...

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), 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 ...

principles and engineering excellence. Experience with the latest AI engineering approaches such as prompt engineering, retrieval-augmented generation (RAG), agentic AI, model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering ...

building or 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, prompt ...

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 ...

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 ...

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 ...

lifecycle, from understanding the business problem and shaping the solution through to deployment, monitoring and continuous improvement.Your work could include building a retrieval-augmented generation solution that helps customer service agents access the right information, developing an agentic workflow that automates campaign planning … applications end to end, from problem definition through to live service and iteration.Develop generative AI solutions using prompt and context engineering, coding, retrieval-augmented generation, fine-tuning and applying evaluation techniques.Implement and optimise agentic AI workflows and multi-step reasoning pipelines, integrating them with ...

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 ...

from understanding the business problem and shaping the solution through to deployment, monitoring and continuous improvement. Your work could include building a retrieval-augmented generation solution that helps customer service agents access the right information, developing an agentic workflow that automates campaign planning … from problem definition through to live service and iteration. Develop generative AI solutions using prompt and context engineering, coding, retrieval-augmented generation, fine-tuning and applying evaluation techniques. Implement and optimise agentic AI workflows and multi-step reasoning pipelines, integrating them with enterprise systems ...

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/ ...

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/ ...

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 ...

frameworks and 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 … time.Strong foundation in a 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 ...

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 frameworks ...