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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Modeller - **Company:** Capgemini - **Location:** London, UK (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computing Platforms, Big Data, Cloud Database, Data as a Services, Data Architecture, Information Engineering, Decision Support Systems, Digital Assets, Graph Database, Knowledge Management, Machine Learning, Meta-Data Management, Operational Data Store, OpenAI, Service-Oriented Architecture, Enterprise Data Management, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, IT Architecture, Generative AI, Agentic-AI, Data Strategy, Data Layers, Event Driven Architecture, AI Platforms, Kubernetes, Real Time Data, Azure AI, Api Design, Model Inference, Databricks - **Published:** October 22, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=235872fe1b12d861 ## About the Role * 12+ years of experience in Enterprise Data, Data Architecture, AI, or Technology Leadership roles. * Proven experience developing enterprise AI and data strategies. * Extensive experience designing large-scale data platforms and modern data architectures. * Strong experience with Databricks, cloud data ecosystems, and lakehouse architectures. * Experience implementing AI, GenAI, Machine Learning, or Agentic AI solutions in enterprise environments. * Strong understanding of Knowledge Graphs, Semantic Technologies, Taxonomies, and Metadata Management. * Experience designing API-driven and service-oriented architectures. * Expertise in Data as a Product principles and operational data architectures. * Experience leading enterprise transformation initiatives. * Strong stakeholder management and executive communication skills. * Deep understanding of AI delivery, governance, and adoption challenges. Desirable * Insurance industry experience across Underwriting, Claims, Pricing, Risk, or Actuarial domains. * Experience with Agentic AI platforms and orchestration frameworks. * Experience implementing Enterprise Knowledge Graphs. * Familiarity with Vector Databases, RAG architectures, and LLM ecosystems. * Experience with Azure AI, OpenAI, AWS AI, or Google AI platforms. * Knowledge of event-driven architectures and real-time data platforms. * Experience delivering AI-powered decision support systems. We are a Disability Confident Employer: Capgemini is proud to be a Disability Confident Employer (Level 2) under the UK Government's Disability Confident scheme. As part of our commitment to inclusive recruitment, we will offer an interview to all candidates who: * Declare they have a disability, and * Meet the minimum essential criteria for the role. ## Description we are seeking AI Lead - Data & Agentic AI Foundations who will provide both strategic direction and technical leadership to establish the critical data, knowledge, and technology foundations required to enable AI-powered underwriting, claims, pricing, and operational decision-making. This role will bridge traditional enterprise data platforms and emerging AI ecosystems, including Agentic AI, Knowledge Graphs, Intelligent Automation, and AI-driven decision support. Working closely with Data, Architecture, Technology, and Business stakeholders, the successful candidate will define the target-state AI architecture, establish enterprise AI-ready data foundations, and drive the adoption of scalable AI capabilities across the organization. The role requires a rare combination of strategic vision, enterprise architecture expertise, hands-on technical leadership, and insurance domain knowledge. Hybrid working: The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of Company offices, client sites, and your home; noting that you will be unable to work at home 100% of the time. Your Role: AI Strategy & Transformation * Define and execute the enterprise roadmap toward becoming an AI-native insurance organization. * Develop long-term AI and data architecture strategies aligned with business objectives. * Partner with the CDO, CIO, Data Engineering, Enterprise Architecture, and Underwriting leadership teams to prioritize AI investments and initiatives. * Create phased roadmaps covering near-term, medium-term, and future-state AI capabilities. * Identify opportunities where AI can improve underwriting effectiveness, claims efficiency, pricing sophistication, and operational productivity. * Establish governance frameworks supporting responsible and scalable AI adoption. Agentic AI Architecture & Foundations * Design and implement foundational capabilities enabling enterprise Agentic AI adoption. * Define standards for exposing business capabilities, enterprise applications, and data assets as reusable AI services and tools. * Establish agent orchestration patterns, tool integration standards, and autonomous workflow architectures. * Develop frameworks supporting AI agents, copilots, and intelligent assistants across business functions. * Evaluate emerging AI technologies and agent frameworks to support enterprise-scale adoption. Data as a Product Leadership * Champion and institutionalize a Data as a Product operating model. * Define AI-ready data products across: + Underwriting + Claims + Pricing + Actuarial + Risk Management + Operational Decision Support * Ensure data products are discoverable, reusable, governed, trusted, and consumable by AI applications and agents. * Drive transition from traditional reporting-focused data usage toward operational and decision-centric AI use cases. * Promote product thinking across data teams and business stakeholders. AI-Ready Data Platform Architecture * Assess current Databricks and Enterprise Data Platform capabilities against AI requirements. * Define enhancements required to support: + AI and GenAI workloads + Retrieval-Augmented Generation (RAG) + Large Language Models (LLMs) + Agentic workflows + Real-time intelligence solutions * Design and implement operational data zones supporting: + Real-time and near real-time data access + AI inference workloads + Event-driven architectures + Intelligent automation solutions * Establish approaches for exposing enterprise data through: + APIs + Data Services + Semantic Layers + AI Interfaces Knowledge Platforms + Knowledge Management & Semantic Architecture * Define enterprise strategies for managing structured and unstructured knowledge assets including: + Underwriting Guidelines + Risk Appetite Frameworks + Policy Documentation + Product Catalogues + Regulatory Documentation + Clause Libraries + Compliance Standards * Lead the design and implementation of enterprise Knowledge Graphs. * Establish semantic data models and contextual knowledge layers. * Build scalable knowledge foundations supporting AI applications and intelligent reasoning. * Ensure trusted, governed, and auditable access to contextual business knowledge.