01Overview
Regional AI & Data Hub Technical Lead Bureau Veritas | Global AI & Data Transformation Regional AI & Data Hub Technical Lead Role Briefing regional technical leadership, solution architecture, engineering quality, and foundation contribution 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 foundationsnot 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 Technical Lead provides hands-on technical leadership for one regional hub, translating validated journey-level opportunities into robust AI solution architectures and guiding engineering work from prototype through industrialized deployment and ongoing improvement. The role is accountable for GenAI and agentic solution design, AI engineering quality, model and workflow evaluation, data and context architecture, integration with enterprise platforms, production monitoring, and contribution of reusable AI components back into the Shared AI technical foundation. Each Regional AI & Data Hub has one Technical Lead, working as a leadership pair with the Regional AI & Data Hub Manager. The Technical Lead operates under the technical guidance of the Chief Technical Architect, Enterprise AI , the Director, Data Science , and the Director, AI Context Fabric & Semantic Platform , ensuring alignment with enterprise architecture, data science standards, semantic platform standards, and the Shared AI technical foundation. Nature of the Role This is a senior hands-on technical leadership role for an AI builder who can move fluidly between strategy, architecture, and implementation. It requires deep practical judgment across GenAI, agentic systems, data science, model orchestration, retrieval and context patterns, evaluation, integration, and production operations. The role must help teams turn promising AI prototypes into reliable, evaluated, observable, safe, and reusable enterprise capabilities. Core Accountabilities Technical discovery & solution framing. Partner with the Hub Manager and business stakeholders to translate validated journey-level opportunities into feasible technical approaches, solution options, and implementation trade-offs. Assess data readiness, integration complexity, model and agentic AI suitability, risk, evaluation requirements, and reuse potential before build work begins, while keeping early design choices grounded in end-to-end workflows, user needs, enterprise architecture, and certified human judgment boundaries. AI builder expertise & applied technical judgment. Serve as the hubs senior AI builder and technical reference, bringing hands-on expertise in designing and developing GenAI applications, agentic workflows, AI copilots, data science solutions, retrieval-augmented generation, model orchestration, prompt and tool design, evaluation harnesses, and AI-enabled workflow automation. Guide teams on architecture, model and tool selection, context and retrieval patterns, guardrails, integration, observability, and production readiness, ensuring that AI capabilities are not only conceptually compelling but practically buildable, maintainable, reusable, and safe for enterprise deployment. Field-informed technical validation. Ground technical decisions in direct understanding of end-to-end user workflows, edge cases, data realities, system constraints, handoffs, and operational risk. Use field insight to guide architecture, data and integration decisions, evaluation design, technical trade-offs, and progression from prototype to production, ensuring solutions remain reliable, usable, observable, and safe in the workflows where they are used. AI solution architecture, engineering quality & industrialization. Lead technical design across GenAI, agentic systems, data science, data engineering, semantic and context layers, integration, workflow automation, model orchestration, evaluation, monitoring, and deployment patterns. Guide engineers in building scalable, maintainable, secure, observable, and .
