326 to 350 of 498 Retrieval-Augmented Generation Jobs in London

working with technologies including Azure Databricks, Azure Machine Learning, Azure AI Foundry, LangChain, LangGraph and Python, building enterprise AI applications using RAG, vector databases, prompt orchestration and tool‐calling patterns. You’ll need experience with: Deploying LLMs and machine learning models into Azure enterprise environments. Azure Databricks, Azure Machine Learning … ideally Azure AI Foundry. Building production AI applications using LangChain, LangGraph and Python. MLOps/LLMOps including deployment, monitoring, governance and lifecycle management. RAG, vector databases, prompt engineering and tool‐calling patterns. Building end-to-end AI/ML pipelines from data preparation through to production. Working with technical ...

hackajob is partnering directly with Accenture to hire for this role. Role Description We are building the next generation of AI-native engineering talent engineers who use AI as a core part of how they work, not as an add-on. As an AI Engineer (Software), you will … improve AI-generated outputs; understanding of AI limitations and responsible use Familiarity with agentic system concepts awareness of orchestration frameworks (LangChain,LangGraph, or equivalent), RAG pipelines, and how full-stack applications connect to agent-basedarchitecture; production experience preferred, conceptual understandingrequired #LI-MP About Accenture Accenture is a leading global professional ...

early, and drive progress without being pushedConsulting or client-facing delivery experience is a strong advantageExposure to GenAI applications in decisioning — personalised content generation, AI-driven offer selectionPreferredExperience evaluating purpose-built decisioning solutions and contributing to capex/opex business casesFamiliarity with real-time streaming technologies … Required — Technical SkillsAdvanced Python — writing clean, production-grade code, not just scripts (OOP, async, packaging, testing)Generative AI & LLM development — prompt engineering, fine-tuning, RAG pipelines, context window managementAgentic AI frameworks — LangChain, LangGraph, AutoGen, or similar; building multi-step autonomous workflowsMachine Learning — model development, training, evaluation, and deploymentAPIs & backend development ...

/GCP, contributing production-ready code as part of a delivery team. Build and evaluate AI/LLM solutions, including prompt/RAG patterns and agentic workflows, alongside classical ML where appropriate. Apply ML techniques to forecasting, segmentation/CLV, causal inference, and other business problems. Analyse large, heterogeneous datasets … technical input to risk, privacy, and controls assessments. Required qualifications, capabilities and skillsFamiliarity with AI/LLM development patterns and frameworks, including prompt engineering, RAG, LangChain/LangGraph and/or Google ADK.Strong Python and SQL; able to write clean, production-ready code. Strong AI/ML fundamentals, including statistics ...

evaluation, and multi-agent patterns, and you understand the trade-offs involved. Experience with RAG systems – You have a solid understanding of retrieval-augmented generation systems and pipelines, and you've built them in real-world contexts. Cloud platform experience – You're experienced with … doIn this role, you will: Design and build cutting-edge AI applications – from MCP servers and voice-to-voice real-time apps to RAG bots and multi-agent systems. You'll work on technology that's genuinely new territory. Advise clients on AI strategy – join workshops, contribute to proposals ...

including typed data models, APIs, automated testing and maintainable system design. Hands-on experience with hosted LLM APIs, structured outputs, tool use, retrieval-augmented generation, context management and model limitations. Experience evaluating non-deterministic systems using representative datasets, explicit metrics, failure analysis and regression ...

improve Design and ship multi-agent systems and production-grade automations that measurably improve how internal teams and customer-facing staff work Build RAG pipelines for internal knowledge retrieval that ground agents in real organisational context Keep the AI tooling stack healthy as usage scales across 2,500+ … development Hands-on experience building multi-agent systems using agentic frameworks — LangChain, LangGraph, MCP, Google ADK or similar Experience with RAG pipelines — retrieval-augmented generation for internal knowledge and workflow automation Background in enterprise tooling — rapid prototyping tools (Vercel/Railway), identity automation (Entra ...

offerings. Deep understanding of modern LLM agent architectures, including multi-agent systems, tool-augmented reasoning, memory and retrieval, programmatic orchestration, RAG, and long-horizon executionStrong grasp of agentic evaluation design, including how to measure task completion, failure recovery, and long-horizon reliability, and how to diagnose ...

focused on production AI: grounded assistants, agentic workflows, evaluation harnesses, and the governance controls that make them safe to run. Design production AI systems: RAG pipelines, agent workflows, tool integrations. Define evaluation, monitoring, and rollback strategies for AI features. Advise clients on model selection, vendor trade-offs, and build ...

extract patterns from one‐off work and turn them into reusable assets for others. Applied AI and Agentic Systems Experience building with LLMs, agents, RAG, tool use, workflow orchestration, or automation systems. Ability to reason about multi‐agent, human‐in‐the‐loop, and enterprise integration patterns. Comfort debugging agent behavior ...

Capgemini is seeking a GenAI Architect in London to lead architecture and delivery of enterprise-scale AI solutions on AWS, leveraging Bedrock and RAG techniques. You will shape reusable patterns for prompt orchestration, agentic workflows, and observability while working with security, compliance and engineering teams. The role emphasizes governance, model ...

Experience within Azure environments Strong stakeholder engagement in large enterprise environments Experience in regulated industries is highly desirable Exposure to Neo4j, Microsoft Graph, GraphRAG, RAG or vector databases would be beneficial. ...

across frontend and backend development, with a solid understanding of system design and API architecture. Experience building AI-powered applications, including working with LLMs, RAG systems, and related technologies. Demonstrated ability to take ownership of features from concept through production, making pragmatic engineering decisions while thriving in fast-paced, evolving ...

years of experience managing large tech, cloud, or consulting projects. Mandatory Tech Skills: Real, hands-on experience with AWS AI services-specifically Amazon Bedrock (RAG, prompt engineering) and AgentCore . Service Management: A strong background in SIAM and ITSM frameworks. Leadership: proven ability to comfortably talk tech with developers ...

years of experience managing large tech, cloud, or consulting projects. Mandatory Tech Skills: Real, hands-on experience with AWS AI services-specifically Amazon Bedrock (RAG, prompt engineering) and AgentCore . Service Management: A strong background in SIAM and ITSM frameworks. Leadership: proven ability to comfortably talk tech with developers ...

Engineer, you will build user-facing applications and backend services that integrate with Model Context Protocol (MCP) servers, LLM-based applications, RAG-based retrieval systems, and agent orchestration platforms. You will work closely with AI developers, platform engineers, product teams, and other stakeholders to deliver scalable, secure, observable … tools, skills, APIs, and UI components for use across AI applications and agent runtimes. Integrate AI-powered user interfaces with backend APIs, MCP servers, RAG systems, and agent workflows. Implement RBAC policies governing which users, agents, clients, and applications can invoke specific tools or services. Develop agent workflows that support ...

efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve. Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG/context-graph retrieval, memory, tool/function calling, MCP integration and multi-agent orchestration. Integrate to standards: connect agents into client ecosystems … Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework/Semantic Kernel). Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG/context graphs, tool/function calling, MCP, and multi-agent orchestration. Eval-driven development: designing evaluation harnesses and measuring agent quality, safety and reliability. ...

efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve. Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG/context-graph retrieval, memory, tool/function calling, MCP integration and multi-agent orchestration. Integrate to standards: connect agents into client ecosystems … Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework/Semantic Kernel). Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG/context graphs, tool/function calling, MCP, and multi-agent orchestration. Eval-driven development: designing evaluation harnesses and measuring agent quality, safety and reliability. ...

will be responsible for turning business use cases into scalable, production-ready AI and data solutions. This will include building AI agents, developing RAG capabilities, automating workflows and creating data products that support balance sheet modelling and wider business objectives. The role is highly hands-on and will suit … automation while taking solutions from initial concept through to production. Key Responsibilities Design and build production-ready AI agents and agentic workflows. Develop RAG solutions and AI-powered applications. Translate business requirements and use cases into scalable technical solutions. Build and maintain data pipelines and reusable data components. Develop balance ...

platform - including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimisation, and other post-training and agentic techniques - enabling the next generation of AI-powered products, agents, automation, and personalisation. We're excited about you because... BSc, MSc or PhD in Computer Science or equivalent 5+ … internal platforms, or self-serve infrastructure Experience building and deploying AI agents or MCP servers in production Experience with eval systems, LLM observability, tracing, RAG, search, or vector databases Diversity, Equity and Inclusion At Deliveroo, we know that a great workplace reflects the world around us and that true diversity ...

production AI from one platform. You'll join a strong team of engineers and data scientists to build and scale the next generation of Homesearch's data and AI platform. Working with large property datasets, you'll automate and improve data pipelines, strengthen our platform foundations, and build … both live products and AI/ML workloads. Property, listing or other entity-resolution data. Search and indexing: Elasticsearch, OpenSearch or similar. LLMs, RAG, AI agents and the major AI application frameworks. How We Work Our values run through everything we do. Remove Friction: make everything seamless. Stay Curious ...

evolving our AI products Synthesize qualitative and quantitative insights into problem statements that identify root causes, not just symptoms Shape the next generation of agentic AI products: multi-step reasoning workflows, tool orchestration, and autonomous task completion for financial professionals Demonstrate a working understanding of the challenges specific … product management experience, preferably in FinTech or B2B SaaS, with at least 2 years focused on ML or GenAI products Working knowledge of RAG architectures, embeddings, prompt engineering, and LLM evaluation methods (not just "AI concepts" at a high level) Experience shipping AI products where output quality is probabilistic ...

value. Establish and maintain platform implementation standards and metadata schemas that keep the knowledge model accurate, governed, scalable and fit for search, retrieval-augmented generation, automated classification and other AI-enabled services. Partner with Practice Group SMEs and Knowledge teams to capture and validate … experience designing, governing or operationalising ontologies, taxonomies, metadata models, knowledge graphs or structured knowledge frameworks, including their use in AI-enabled search, retrieval-augmented generation, automated classification or knowledge discovery. Strong understanding of data quality, metadata management, content lifecycle governance, access controls and information ...

value. Establish and maintain platform implementation standards and metadata schemas that keep the knowledge model accurate, governed, scalable and fit for search, retrieval-augmented generation, automated classification and other AI-enabled services. Partner with Practice Group SMEs and Knowledge teams to capture and validate … experience designing, governing or operationalising ontologies, taxonomies, metadata models, knowledge graphs or structured knowledge frameworks, including their use in AI-enabled search, retrieval-augmented generation, automated classification or knowledge discovery. Strong understanding of data quality, metadata management, content lifecycle governance, access controls and information ...

qualifications. Tech focus: Python and AWS (or equivalents in Azure or GCP), with hands‐on work across classical ML and modern LLM/RAG systems using services like Amazon SageMaker and Bedrock. What you'll doPartner with stakeholders across the business to explore high‐impact opportunities. Own the full … lifecycle: problem framing, data discovery, feature engineering, modelling, evaluation, deployment, monitoring, and iteration. Build and productionize LLM features where appropriate (retrieval‐augmented generation, evaluation, safety guardrails, cost/latency optimization) on AWS. Contribute to DS/ML standards: experimentation, model governance, documentation, and reproducibility. ...