76 to 100 of 500 Retrieval-Augmented Generation Jobs in London

data systems. Strong understanding of AI-ready data architectures supporting analytics, machine learning, generative AI, Retrieval-Augmented Generation (RAG), vector databases, and knowledge graphs. Experience with sovereign data ecosystems and interoperability frameworks such as GAIA-X, IDSA, and DSIF, with the ability to innovate ...

coverageYour qualificationsEarly-career professional with proven commercial experience in: Troubleshooting Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) applicationsRobust prompt and context engineeringData analysis (Excel, Anaplan or similar tools)Software development or quality assuranceLevel 2 or level 3 technical supportA degree in Mathematics ...

unstructured data. Design and deliver LLM-powered applications: conversational AI, enterprise copilots, knowledge management, and workflow automation. Architect advanced retrieval using RAG, Agentic RAG, Graph RAG, knowledge-graph, and hybrid approaches. Design multi-agent systems with modern orchestration and reasoning architectures, including human-in-the-loop and autonomous … Expected Programming & engineering : expert Python; REST APIs and microservices; FastAPI; distributed systems; cloud-native architecture. LLM application delivery: prompt engineering, tool and function calling, RAG, and at least one production Agentic AI system. Cloud AI platform : deep experience with one of Azure, OpenAI, AWS Bedrock, Claude; Kubernetes and cloud-native ...

skills use, and memory systems and work hands-on with foundation models through fine-tuning, retrieval-augmented generation (RAG), and custom model integration. A partof your work will also involve engineering the AI context layer that makes these systems intelligent in practice connecting enterprise … faithfulness, translating findings into design improvements Architect and implement foundation model integrations selecting the right models, invocation patterns, and customization approaches (fine-tuning, RAG, custom integration) based on capability, cost, and performance trade-offs Design and build model adaptation and fine-tuning pipelines, applying working knowledge of transformer-based architectures ...

Discovery Optimisation Continuous optimisation of our job search experience, leveraging advanced retrieval and ranking algorithms. Research, implement, and experiment with next-generation search techniques to enhance relevance and accuracy for users globally. Collaborate with engineering teams to integrate data science models seamlessly into our tech stack. … require sponsorship. Nice to have: Experience of agentic frameworks Experience with advanced search techniques, specifically Retrieval-Augmented Generation (RAG) and vector search. Prior experience working within the online classified advertising or job board industry. An active personal website or Github account with examples ...

that means leading the team(s) across surfaces like: Diagnosis & needs assessment — AI-heavy services (retrieval-augmented generation (RAG), LLM-scored assessment) that turn raw signal into a picture of where a learner or employer actually is. Prescription & eligibility — build the product which helps … out. Stack & how we leadTypeScript core (React/Next.js, Node) with PostgreSQL and RabbitMQ as the event backbone, plus Python and Elixir services — from RAG pipelines to a Claude-powered edge-native product. GitHub, CircleCI, Datadog. AI-assisted development is first-class, not bolted on. You'll lead full-stack ...

APIs and backend services that power enterprise customer integrations Owning the LLM application layer, including retrieval-augmented generation (RAG) workflows Improving model performance, reliability, evaluation frameworks, monitoring, and cost optimisation Working across the stack to deliver customer-facing features and platform capabilities Collaborating with … engineering expertise, ideally within API-first products Experience with Node.js and TypeScript, or a proven ability to transition successfully between technology stacks Understanding of RAG architectures, evaluation frameworks, guardrails, monitoring, and model optimisation Experience managing token usage, infrastructure costs, and scalability challenges in AI products Comfortable working autonomously ...

platforms. Hands-on experience with AI orchestration and agent frameworks including LangChain, LangGraph, Semantic Kernel, LlamaIndex, CrewAI, and AutoGen. Strong knowledge of Prompt Engineering, RAG, Vector Databases (Pinecone, Weaviate, Chroma, FAISS, Azure AI Search), Model Fine-Tuning, LLMOps, and Secure AI practices. Enterprise Architecture & Security Proficiency in enterprise architecture frameworks ...

build scalable agentic AI platforms that integrate Large Language Models (LLMs), multi-agent orchestration and retrieval-augmented generation (RAG) patterns into production-ready enterprise solutions. You will focus on creating reusable platform components, orchestration engines and governance frameworks that allow complex AI workflows ...

shipping AI-powered products end-to-end using LLM technologies. Owning and scaling the retrieval-augmented generation (RAG) and AI application layer. Developing secure, production-grade systems including guardrails, evaluation frameworks, monitoring, caching, and token cost optimisation. Working across the stack, including backend services ...

development and deployment of cutting-edge Gen AI models and solutions. You will be responsible for building, testing, and maintaining our Gen AI, RAG and NLP solutionsYou will work throughout the whole life cycle of data science projects: design, implementation, production and beyond. You will deliver efficient and production-ready … science pipelines (such as pre-processing and model inference) and their deployment together with software engineering teamOptimizing and customizing Retrieval Augmented Generation (RAG) pipelines to meet specific project requirements that involve content ingestion, machine translation, and contextualized information retrievalIngesting, preprocessing, and transforming large-scale ...

automation and optimisation. Design and build AI solutions using technologies such as LLMs, agents and retrieval-augmented generation (RAG). Create practical roadmaps for AI adoption, including prioritised implementation plans. Engage with business stakeholders to communicate opportunities, risks and recommendations. Develop, deploy and scale … delivering end-to-end AI solutions in commercial environments. Capability to work across strategy, architecture and hands-on engineering. Expertise with LLMs, AI agents, RAG frameworks and modern AI tooling. Experience assessing business processes and translating requirements into technical solutions. Strong stakeholder management and communication skills. Background working within smaller ...

Agentic AI Multi-agent systems Tool calling Function orchestration Workflow automation Planning and reasoning patterns Human-in-the-loop design Knowledge & Retrieval RAG architectures Vector databases Semantic search Enterprise search Document ingestion Knowledge graphs (advantageous) Integration APIs Event-driven architectures Microsoft ecosystem ServiceNow CRM/ERP integration Identity ...

Azure AI Experience building Agentic AI workflows, orchestration frameworks, AI copilots, or autonomous process automation solutions Knowledge of AI-driven APIs, semantic search, RAG architectures, and vector database integrations Azure certifications such as AZ-900, AZ-204, or AI-900 would be advantageous Key Responsibilities Design, develop, and maintain scalable ...

data engineering, ML engineering, solution engineering, technical consulting, or related hands-on technical roles.2+ years of experience building or deploying AI, ML, GenAI, LLM, RAG, agentic AI, data science, or automation solutions. Hands-on experience with programming languages and frameworks such as Python, JavaScript/TypeScript, FastAPI, Flask, React, LangChain ...

Role Join our AI Engineering division in London to specialize in LLM fine-tuning, retrieval-augmented generation (RAG), and hosting private models. You will be responsible for tailoring deep learning models to specialized domain tasks. Key Responsibilities Fine-tune open-source models (Llama, Mistral ...

delivered across the organization. Key Responsibilities: Lead the design and build of end-to-end generative AI applications using LangGraph, including agentic workflows, RAG pipelines, and LLM-integrated services. Define and drive architecture for scalable, secure, reliable, and maintainable AI solutions deployed in production. Develop robust, well-tested backend APIs ...

cloud‐native delivery patterns What Will Make You Stand Out Experience building, deploying, and evaluating multi‐agent AI solutions, agent orchestration patterns, RAG architectures, and enterprise integrations Knowledge of agent communication protocols and interoperability patterns, such as MCP and A2A Ability to balance innovation with practical delivery considerations, including risk ...

S3. Strong understanding of MLOps tooling, including MLflow, Weights & Biases, or Data Version Control (DVC). Experience developing LLM or Generative AI applications, including RAG architectures and prompt engineering. Knowledge of LLMOps frameworks such as LangChain, LangGraph, or LangSmith. Experience taking ML models from experimentation into production environments. Strong communication ...

concepts from idea to production.Strong knowledge of deep learning, NLP, and LLM architectures and experience in fine-tuning LLMs or training domain-specific models, RAG, embeddings, and vector databases.Familiarity with agentic AI concepts and orchestration frameworks.Experience deploying AI solutions on cloud platforms such as Azure, AWS, or GCP.Strong problem-solving ...

deploy production-grade AI and ML solutions, including GenAI and LLM-enabled systems. Shape AI solution architectures, selecting appropriate patterns (e.g. batch vs streaming, RAG, feature stores, model serving).Lead AI/ML workstreams end-to-end, including planning, delivery governance, and stakeholder management. Mentor and support AI engineers, data ...

across the others: Agentic AI & LLM Engineering: Design and build production-grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering at scale. Strong opinions on secure, resilient enterprise deployment of LLM-powered systems; current knowledge of the latest model capabilities ...

across the others: Agentic AI & LLM Engineering: Design and build production-grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering at scale. Strong opinions on secure, resilient enterprise deployment of LLM-powered systems; current knowledge of the latest model capabilities ...

contribute high-quality production code while guiding technical directionDeep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniquesProven ability to take complex AI solutions from prototype … inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scaleImplement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integrationMake key technical decisions while remaining accountable for practical ...

high-quality production code while guiding technical direction Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype … pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...