Regional AI & Data Hub Manager Details | Bureau Veritas

Regional AI & Data Hub Manager Date: 3 Aug 2026 Location: Noida, Uttar Pradesh, IN Company: Bureau Veritas Bureau Veritas | Global AI & Data TransformationRole Briefing Regional hub leadership, business partnership, delivery accountability, and adoption Context & Purpose The Regional AI & Data Hubs are where validated business demand becomes working, enterprise-grade AI capabilities embedded in core business workflows. Each hub pairs business-facing leadership with deep technical AI leadership: the Regional AI & Data Hub Manager ensures the hub is focused on the right end-to-end journeys, delivers value, builds capability, and drives adoption; the Regional AI & Data Hub Technical Lead ensures that AI solutions are technically sound, evaluated, reusable, scalable, secure, observable, and aligned to enterprise standards. The hubs operate with a field-informed delivery model, grounding priorities and solution designs in direct understanding of real user workflows, operational constraints, adoption barriers, and value drivers. Their focus is on transforming end-to-end journeys and ways of working through GenAI, agentic workflows, data science, automation, and enterprise data foundations-not simply delivering isolated use cases. Together, they turn clear, high-value problems into deployed AI capabilities and reusable enablers for the Shared AI technical foundation. Role Mission The Regional AI & Data Hub Manager leads one of Bureau Veritas's regional AI and data delivery hubs from a business, solution, delivery, adoption, and team leadership perspective. The role is accountable for shaping demand with regional and business stakeholders, prioritizing the right end-to-end journeys rather than isolated use cases, ensuring delivery discipline, building and leading the hub team, and making sure AI-enabled capabilities-including GenAI applications, agentic workflows, data science solutions, automation, and reusable enterprise AI components-are technically credible, adopted, operated, evaluated, and continuously improved in real business workflows. There are three Regional AI & Data Hubs - Americas, France, and Asia. Each hub has two key leadership roles: one Regional AI & Data Hub Manager and one Regional AI & Data Hub Technical Lead. The hubs place build capacity close to the businesses and regions of the matrix, while the Shared AI technical foundation, reusable solution archetypes, and governance spine ensure that all three hubs deliver to one common enterprise standard rather than diverging into separate local approaches. The Regional AI & Data Hubs work under the technical guidance of the Chief Technical Architect, Enterprise AI, the Director, Data Science, and the Director, AI Context Fabric & Semantic Platform, ensuring that regional delivery remains aligned with enterprise architecture, data science standards, semantic platform standards, and the Shared AI technical foundation. Nature of the Role This is a senior hub leadership role for a business-oriented technology leader with meaningful AI solution fluency and practical understanding of enterprise data capabilities. It requires the ability to translate business priorities into a clear portfolio of AI-enabled journey transformation work, understand GenAI, agentic, data science, automation, integration, evaluation, and deployment implications, engage directly with working prototypes and delivery teams, lead multidisciplinary teams, manage delivery trade-offs, and ensure that AI solutions create measurable value after deployment. Core Accountabilities Demand shaping & AI solution framing: Partner with business stakeholders to convert priorities into clear problem statements and transformation opportunities, focusing on end-to-end journeys, business impact, and new AI-enabled ways of working rather than isolated use-case delivery. Prioritize work by value, feasibility, data readiness, model and agentic AI suitability, technical complexity, scalability, evaluation requirements, risk, and alignment with the AI strategy, using sufficient AI solution fluency to challenge assumptions, shape viable opportunities, and guide scale-up decisions. Field immersion, workflow understanding & value validation: Ground hub priorities and solution designs in direct observation of real user workflows, operational constraints, adoption barriers, and value drivers. Use field insight to sharpen problem statements, keep hub work anchored in operational reality, and validate deployed solutions against adoption, workflow impact, operational outcomes, and business value. Portfol .