1 to 25 of 500 Retrieval-Augmented Generation Jobs in London

improve how users discover, synthesize, and interact with scientific knowledge.Our work spans semantic search, retrieval-augmented generation (RAG), ranking systems, LLM evaluation, online experimentation, and scalable evaluation infrastructure. We partner closely with Product, Engineering, UX Research, Knowledge Graph, and Applied AI teams to deliver … + Search relevance and ranking quality. + Semantic retrieval and vector search + Retrieval-augmented generation (RAG) + AI grounding and hallucination mitigation + User discovery and engagement outcomes* Establish scalable evaluation methodologies for search, retrieval, recommendation, and LLM-powered ...

team. The ideal candidate will have a deep understanding of Generative and Agentic AI, LLMs, retrieval-augmented generation (RAG), machine learning, and modern interoperability standards such as the Model Context Protocol (MCP), along with a proven track record of architecting and implementing innovative, enterprise … skills that let agents perform specialized, domain-specific tasks reliably, securely, and consistently across teams. Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval ...

services.* Drive adoption of AI/LLM technologies across Private Markets products, including prompt engineering, Retrieval-Augmented Generation (RAG), AI-assisted workflows, and intelligent automation where they deliver measurable business value.* Lead and develop a team of engineers, fostering a strong engineering culture focused … Experience with AI/ML technologies and hands-on knowledge of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, or similar Generative AI technologies is highly desirable.* Familiarity with AI orchestration frameworks (e.g. LangChain, Semantic Kernel, AutoGen, or similar) would ...

learning and large language models (LLMs) using frameworks such as PyTorch, TensorFlow, and Keras. Develop Retrieval-Augmented Generation (RAG) applications using LangChain, Hugging Face, and LlamaIndex. Implement and optimize vector database, semantic search, and hybrid search solutions for AI-powered applications. Perform model fine … model repositories Proficiency in LangChain and LlamaIndex for building AI-powered workflows Experience implementing Retrieval-Augmented Generation (RAG) frameworks Knowledge of Vector Databases and Hybrid Search techniques Strong understanding of Natural Language Processing (NLP) concepts and applications Experience with data processing, transformation, and feature ...

solutions using modern Azure technologies and cloud-native architecture. Integrate Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and Agentic AI capabilities into business applications. Develop responsive frontend applications alongside robust APIs and backend services to create seamless user experiences. Work closely with Product … Services and cloud-native application development. Experience implementing AI solutions including LLM integration, prompt engineering, Retrieval-Augmented Generation (RAG) and vector databases. Experience working with SQL and NoSQL databases, with knowledge of platforms such as SQL Server, Snowflake, OneLake or similar data platforms. Strong ...

products. Partner closely with engineering teams to build intelligent experiences leveraging Large Language Models (LLMs), retrieval-augmented generation (RAG), conversational interfaces, and agentic workflows. Collaborate with analytics, data, engineering, and business stakeholders to understand customer needs and identify opportunities to create value through … Language Models (LLMs), conversational AI, or machine learning-powered experiences. Understanding of concepts such as retrieval-augmented generation (RAG), AI agents, orchestration frameworks, evaluation approaches, or similar AI product patterns. Strong analytical and problem-solving skills, with the ability to use data and customer ...

enabled products Partner with engineering teams to build intelligent experiences using Large Language Models (LLMs), retrieval-augmented generation (RAG), conversational interfaces, and agentic workflows Collaborate with analytics, data, engineering, and business stakeholders to identify customer needs and AI-driven opportunities Translate complex customer … generative AI, Large Language Models (LLMs), conversational AI, or machine learning-powered experiences Understanding of retrieval-augmented generation (RAG), AI agents, orchestration frameworks, evaluation approaches, or similar AI product patterns Strong analytical and problem-solving skills Excellent communication and stakeholder management skills, with ...

agents, knowledge graphs and chatbots to enhance user experience and efficiency. Leading projects that utilize Retrieval-Augmented Generation (RAG) and GraphRAG techniques. Developing and optimizing AI models using machine learning and AI engineering platforms. Mentoring and guiding team members to foster a culture … showcasing these solutions to clients and communicating their business impact. Experience in providing solutions using Retrieval-Augmented Generation (RAG), GraphRAG, knowledge graphs, code agents, deep research agents, and chatbots. Familiarity with AI engineering platforms (Azure AI Foundry, AWS Bedrock, GCP Vertex AI) and employee ...

Technologies team. The candidate must possess deep expertise in Generative and Agentic AI, including LLMs, retrieval-augmented generation (RAG), machine learning, and interoperability standards such as the Model Context Protocol (MCP). The role involves shaping the Generative AI strategy, selecting suitable models … through modular, reusable skills and resources; defining standards for custom tools, connectors and skills ensuring reliable, secure, and consistent operation. Retrieval‐Augmented Generation (RAG) & Knowledge Architecture – expertise in RAG and knowledge‐grounded systems, including chunking, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid ...

Solutions* Design, develop and deploy enterprise-grade AI products and services.* Build agentic AI workflows, retrieval-augmented generation (RAG) solutions and intelligent automation capabilities.* Deliver scalable integrations with LLMs, MCP services, vector databases and enterprise platforms.* Develop reusable AI frameworks, accelerators and engineering standards.Engineering … Python and modern software engineering practices.* Experience with Large Language Models (LLMs) and Generative AI.* Agent frameworks and orchestration technologies.* Retrieval Augmented Generation (RAG) architectures.* API development and microservice design.* Cloud-native development (Azure preferred).* CI/CD automation and MLOps practices.* Vector ...

SolutionsDesign, develop and deploy enterprise-grade AI products and services. Build agentic AI workflows, retrieval-augmented generation (RAG) solutions and intelligent automation capabilities. Deliver scalable integrations with LLMs, MCP services, vector databases and enterprise platforms. Develop reusable AI frameworks, accelerators and engineering standards. Engineering … Technical SkillsPython and modern software engineering practices. Experience with Large Language Models (LLMs) and Generative AI.Agent frameworks and orchestration technologies. Retrieval Augmented Generation (RAG) architectures. API development and microservice design. Cloud-native development (Azure preferred).CI/CD automation and MLOps practices. Vector databases ...

emerging AI capabilities across security operations, leveraging machine learning, generative AI, agentic workflows, and Retrieval-Augmented Generation (RAG) to enhance analysis, automate decision-making, and accelerate security response. As the successful candidate, you will combine strong hands‐on technical expertise with the ability … implementing Artificial Intelligence (AI) capabilities within operational environments, including familiarity with Large Language Models (LLMs), Generative AI, AI agents, and Retrieval‐Augmented Generation (RAG) concepts. Strong knowledge of system integration techniques, REST APIs, authentication mechanisms, and alert‐driven architectures. Experience translating business and operational ...

portions of our AI agent infrastructure , from designing and deploying multi-agent systems to integrating Retrieval-Augmented Generation (RAG) pipelines , and evaluation frameworks . You will be responsible for delivering AI-powered features into production at scale — ensuring they are performant, reliable, and secure … deliver high-impact features. Ensure scalability, performance, and security across the stack. Develop and deploy AI-powered features in production , including RAG (Retrieval-Augmented Generation) systems, multi-agent infrastructure, and evaluation frameworks (Evals) . Create data pipelines for AI model training, evaluation, and continuous ...

will guide cross-functional teams and strategic partners to define reference architectures and delivery roadmaps for multi-agent systems—leveraging LLMs, retrieval-augmented generation, and modern agent frameworks—while instituting governance, safety, and reliability standards and tracking measurable business outcomes. Job responsibilities Architect, develop … tool integration, planning, memory, and workflow orchestration using LangChain, CrewAI, AutoGen, ADK and LangGraph. Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search using vector databases such as Pinecone and Chroma, including indexing, retrieval policies, and evaluation. Build and integrate agent tools ...

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 ...

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 ...

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 ...

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 ...

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 ...