76 to 100 of 123 Retrieval-Augmented Generation Jobs in London

Docker, Kubernetes, and containerized applications, Expert knowledge of TypeScript, Expert knowledge of gRPC (unary, response streaming, bi-directional streaming, REST mapping), Hands-on experience with LLM APIs, embeddings, or RAG patterns Track record of delivering user-facing software at scale. Interview process After submitting your application, the team reviews your CV and statement of exceptional work. If your application passes More ❯

domain expertise in digital advertising (AdTech), with knowledge of sponsored listings, programmatic advertising, auction dynamics, and performance metrics (CPC, CPA, ROAS) You have knowledge of Generative AI (LLMs, embeddings, RAG) and its potential applications in advertising, as well as experience with ML platforms and workflows (e.g., feature stores, model registries, training pipelines) You have deep expertise in competitive landscape analysis More ❯

LLM pipelines, ensuring versioning, monitoring, and adherence to best practices. Drive integration of external knowledge bases and retrieval systems to augment LLM capabilities. Research and Development: Develop RAG architectures, organize complex domain-specific data (e.g., vector databases, knowledge graphs), and implement knowledge extraction. Benchmark and tune state-of-the-art LLMs for performance and cost efficiency. Incorporate trends … least 2 years in a senior or lead role. Proven expertise with modern LLMs in production. Leadership skills: Proven leadership in agile environments, strong communication, mentoring abilities. LLM and RAG expertise: Building RAG architectures, integrating vector and graph databases. Transformer and LLM architectures: Experience with GPT-4, Claude, Gemini, Llama, Falcon, Mistral. Model performance and optimization: Fine-tuning and optimizing More ❯

Architect Contract London 5 Months+ Role Description: Design and evolve enterprise-grade data and AI architectures using Databricks and Azure. Perform GenAI architecture uplift, including LLMOps, agentic frameworks, and RAG-based patterns. Evaluate and evolve data science and platform reference architectures, ensuring strategic alignment. Collaborate with global stakeholders across EO, NAO, and AusPac. Drive architectural governance and technical evaluations to More ❯

and wants to shape how AI is used in the physical world. 🧠 You’ll need: 2+ years' engineering experience with a builder’s mindset Hands-on with LLMs — LangChain, RAG, agents, fine-tuning, etc. Infra knowledge (cloud, containers, relational/graph DBs) Bonus: startup/founder experience 🔧 You’ll be doing: Shipping to prod from Day 1 Building end-to More ❯

and wants to shape how AI is used in the physical world. 🧠 You’ll need: 2+ years' engineering experience with a builder’s mindset Hands-on with LLMs — LangChain, RAG, agents, fine-tuning, etc. Infra knowledge (cloud, containers, relational/graph DBs) Bonus: startup/founder experience 🔧 You’ll be doing: Shipping to prod from Day 1 Building end-to More ❯

systems and significantly impact the direction of a successful early-stage business. Role Overview: Key Responsibilities: AI Software Development: Develop and enhance the company's AI-based (LLM/RAG) software. System Integration: Integrate offline versions of the software into customer systems. Backend Development: Work primarily with Python to build scalable backend systems and APIs. Minimum Qualifications & Experience: Backend Proficiency More ❯

Nice to have Familiar with Peer-to-Peer technologies (Kademlia, bittorent, libp2p) Comfortable with high-availability concepts Rust or C++ skills are a plus Familiar with AI domain applications (RAG, Agents, Inference, AI SDKs) Familiarity with real-time data delivery (NodeJS/other streaming More ❯

data team and creating a culture that enables growth, development and psychological safety Exploring and applying Large Language Models (LLMs) and other modern AI techniques (e.g., embeddings, retrieval-augmented generation, summarisation) to enhance internal tools, automate workflows, or develop client-facing features Acquiring, cleaning, and integrating data from internal and external sources to enrich More ❯

and wants to shape how AI is used in the physical world. You’ll need: 2+ years' engineering experience with a builder’s mindset Hands-on with LLMs — LangChain, RAG, agents, fine-tuning, etc. Infra knowledge (cloud, containers, relational/graph DBs) Bonus: startup/founder experience You’ll be doing: Shipping to prod from Day 1 Building end-to More ❯

and wants to shape how AI is used in the physical world. 🧠 You’ll need: 2+ years' engineering experience with a builder’s mindset Hands-on with LLMs — LangChain, RAG, agents, fine-tuning, etc. Infra knowledge (cloud, containers, relational/graph DBs) Bonus: startup/founder experience 🔧 You’ll be doing: Shipping to prod from Day 1 Building end-to More ❯

edge product Your background looks something like: Engineering experience at tech and product-driven companies Shipping LLM-based solutions to production Experience with inference and evaluation platforms Experience with RAG, semantic search, vector stores, embedding Building features end-to-end with TypeScript, React.js, and Node.js As a person, you Are first and foremost abuilder. Are excited towork in-personfrom our More ❯

teammates, and fueling your curiosity for the latest trends in LLMs, MLOps, and ML more broadly. The impact you will have: Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation Build, scale, and optimize customer data science workloads and apply best in class MLOps … in Databricks Collaborate cross-functionally with the product and engineering teams to define priorities and influence the product roadmap What we look for: Experience building Generative AI applications, including RAG, agents, text2sql, fine-tuning, and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools More ❯

teammates, and fueling your curiosity for the latest trends in LLMs, MLOps, and ML more broadly. The impact you will have: Develop LLM solutions on customer data, such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation Build, scale, and optimize customer data science workloads and apply best-in-class MLOps … in Databricks Collaborate cross-functionally with the product and engineering teams to define priorities and influence the product roadmap What we look for: Experience building Generative AI applications, including RAG, agents, text2sql, fine-tuning, and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools More ❯

and drive AI-led impact. Expand the use of Generative and Agentic AI to improve customer experiences and simplify operations. Upskill teams in Gen AI techniques like prompt design, RAG, fine-tuning, and agentic frameworks. Build reusable Gen AI frameworks in partnership with engineering teams. What Youll Bring Leadership & Collaboration: Experience leading diverse, high-performing teams across Gen AI, ML … Strong communication skills and executive presence to engage stakeholders and uplift capability. Technical Expertise: Hands-on experience applying Generative AI in real-world business settings. Skills in prompt engineering, RAG, guardrail design, orchestration, and tools like LangGraph or Semantic Kernel. Deep knowledge of ML model development, deployment, and evaluation. Proficiency in Python, PyTorch, TensorFlow, SQL, Spark, and AWS tools like More ❯

We are looking for brilliant engineers to join our team at Magentic. We're pushing the boundaries of AI with next-generation agentic systems that can manage entire workflows. We're focusing on a three trillion dollar market of supply chains and procurement. Our mission is to make global manufacturing supply chains robust to an ever-changing world … and event buses (Kafka, Pulsar). Wrangle large, heterogeneous data sets -model, transform, and index multi-modal, multi-terabyte enterprise datasets for advanced workloads Develop enterprise-level next generation AI systems with the support of Magentic's AI specialists Ship complete customer features -from architecture and code to CI/CD, infra-as-code (Terraform), rollout, and user … contract. Thrive in an early-stage, high-ownership environment-prototype today, deploy tomorrow, iterate next week. Bonus Points Experience deploying or consuming LLM-powered services (OpenAI, open-source models, RAG, vector stores) can be a bonus. However, we consider many great candidates without previous AI experience. Familiarity with supply-chain, procurement, or manufacturing domains. Benefits Compensation and Benefits At Magentic More ❯

complex customer projects involving co-training, fine-tuning, and various special projects. • Evaluate and improve the performance of our models on a variety of use cases (e.g., reasoning, code, RAG, tool use, agents) and across modalities (text, image, speech). • Develop complex use cases with our customers, providing guidance on prompting, evaluation, and fine-tuning, and ensuring the best production … data science. • You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products • You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases • You have deep understanding of concepts and algorithms underlying machine learning and LLMs • You're experienced with building and deploying LLMs or NLP applications • You have More ❯

software engineering instincts. Someone who can design intelligent systems that operate reliably and scalably within a complex product environment. What you'll be doing You will explore and implement RAG or Graph RAG approaches to improve retrieval quality and reasoning in LLM workflows, including graph construction, entity linking, and hybrid scoring strategies. Design and implement LLM-powered systems … not just analyses. Experience working in Java or TypeScript environments beneficial. Experience with Claude, OpenAI, Bedrock is nice to have or experience with similar LLM platforms required. Familiarity with RAG, Graph-based retrieval, prompt design, and multi-hop reasoning. Experience deploying LLM-powered features into production environments. Bonus: experience in vector stores, agent orchestration, or legaltech domain knowledge. More ❯

ll take the lead in shaping end-to-end Azure AI architectures, from discovery and PoCs through to delivery and optimisation. You'll work directly with enterprise clients, defining RAG systems, Agentic AI solutions, knowledge base integrations, and driving forward both client success and internal AI accelerators. Key Responsibilities: Design scalable, secure Azure AI architectures tailored to client needs. Work … closely with customer business and technical stakeholders. Define and deliver solutions involving RAG systems, Agentic AI, knowledge bases, and MCP integrations. Lead pre-sales engagements including PoCs, workshops, and stakeholder briefings. Collaborate with engineers, analysts, and scientists to guide delivery. Ensure best practice in responsible AI, data management, and security. Monitor performance and optimise ongoing AI solutions. Contribute to internal More ❯

Architect, youll take the lead in shaping end-to-end Azure AI architectures, from discovery and PoCs through to delivery and optimisation. Youll work directly with enterprise clients, defining RAG systems, Agentic AI solutions, knowledge base integrations, and driving forward both client success and internal AI accelerators. Key Responsibilities: Design scalable, secure Azure AI architectures tailored to client needs. Work … closely with customer business and technical stakeholders. Define and deliver solutions involving RAG systems, Agentic AI, knowledge bases, and MCP integrations. Lead pre-sales engagements including PoCs, workshops, and stakeholder briefings. Collaborate with engineers, analysts, and scientists to guide delivery. Ensure best practice in responsible AI, data management, and security. Monitor performance and optimise ongoing AI solutions. Contribute to internal More ❯

production (e.g., MLOps, CI/CD for ML, model versioning) Build systems that integrate AI into key parts of our stack, such as: Forecasting customer demand and renewable generation Dynamic pricing and energy trading algorithms Intelligent alerts and personalized customer features Work closely with product and engineering leadership to identify high-impact AI opportunities Build and lead a … product teams Clear communication and an ability to prioritize for both experimentation and reliability Bonus Familiarity with optimization, time series modeling, or forecasting Experience with large language models (LLMs), RAG, or generative AI in production Background in MLOps or AI infrastructure at scale Competitive salary and a stock options sign-on bonus Biannual bonus scheme Fully expensed tech to match More ❯

technology and operations, with digital capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values comprise of Stewardship, Best People, Client Value Creation, One Global … Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices e.g. data drift detection, experimentation tracking, RAG, deployment models Building and using cloud-based solutions (networking, security, storage, monitoring, scaling, DR/HA) on one or more cloud platforms SW Engineering and DevSecOps Set yourself apart: Certified More ❯

technology and operations, with digital capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values comprise of Stewardship, Best People, Client Value Creation, One Global … Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices e.g. data drift detection, experimentation tracking, RAG, deployment models Building and using cloud-based solutions (networking, security, storage, monitoring, scaling, DR/HA) on one or more cloud platforms SW Engineering and DevSecOps Set yourself apart: Certified More ❯

growth and investment, specifically within our high-priority systematic trading and analytics systems. We are seeking a hands-on Engineering Lead to drive the development of our next-generation quantitative development platform serving the Equities Cash, Derivatives, and Prime businesses. This is a unique opportunity to make a significant impact as the platform enters its key growth phase. … Docker, Kubernetes) Proven success in enhancing developer experience that reduces friction in coding, building and deploying APIs and client libraries. Real-world application of generative AI prompt engineering and RAG pipelines. Full-stack HTML5 web development skills. Desired Skills: Understanding of Equities Cash, D1 & Deriv market mechanics and products via sell-side trading projects Familiarity with low-latency programming languages More ❯

viewing in real time using image embeddings, similarity search (e.g. CLIP, vector search), and traditional CV approaches (e.g. YOLO, MobileNet). Design and implement pipelines that support retrieval-augmented generation, internal AI tools, and scalable content delivery. Experience with vector databases, agent frameworks, or data workflows is highly relevant. Own model deployment pipelines, including More ❯