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 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 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 hub’s 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 production-ready AI solutions that use approved enterprise patterns, progress from prototype to production, and contribute reusable components back to the Shared AI technical foundation.
- Engineering leadership & technical coaching. Provide hands-on technical guidance to AI engineers, data scientists, developers, and forward-deployed engineers across design, implementation, evaluation, troubleshooting, and production hardening. Establish technical working practices for quality, documentation, reuse, observability, testing, and operational readiness, while promoting disciplined experimentation, peer review, technical accountability, and continuous learning.
- Evaluation, lifecycle operation & continuous improvement. Design and operate evaluation frameworks that measure solution quality, reliability, safety, adoption, business impact, and production behavior over time. Own the technical lifecycle of hub solutions and reusable foundation components in partnership with the Hub Manager, using evaluation and telemetry signals to drive continuous improvement and feed reusable enhancements back into deployed solutions and the Shared AI technical foundation.
- Forward-deployed technical enablement. Enable forward-deployed engineers with patterns, tools, documentation, and technical guardrails that allow local adaptation without fragmenting enterprise architecture. Translate field friction, recurring edge cases, and implementation issues into technical improvements, reusable components, updated engineering standards, and stronger contributions to the Shared AI technical foundation.
Profile
Experience: a track record leading multidisciplinary AI product, GenAI, agentic workflow, data science, data engineering, software, or platform engineering work in an enterprise environment, with direct experience taking AI-enabled solutions from prototype through production operation and improvement.
Mindset & depth: deep AI builder judgment; strong understanding of AI/ML, GenAI, agentic workflows, LLM application patterns, retrieval and context engineering, model orchestration, evaluation, observability, data engineering, integration, and production operations; product thinking for reuse and scale; and a practical orientation toward solving real business problems with AI in operational workflows.
Attributes: technically rigorous, pragmatic, collaborative, comfortable coaching engineers, able to make sound technical trade-offs in ambiguous delivery environments, and committed to grounding technical decisions in real user workflows and production outcomes. For the France hub, professional French and English; language profile varies by hub region.
Positioning, Reporting & Location
Works as the technical counterpart to the Regional AI & Data Hub Manager and operates under the technical guidance of the Chief Technical Architect, Enterprise AI; the Director, Data Science; and the Director, AI Context Fabric & Semantic Platform. One Regional AI & Data Hub Technical Lead per hub. Locations: the Americas, France, and Asia hubs respectively. Occasional travel to BV locations.
Overall Mandate
Provide the technical AI leadership that enables a Regional AI & Data Hub to transform validated end-to-end journeys into reliable, evaluated, scalable, safe, and reusable AI-enabled capabilities, while strengthening the Shared AI technical foundation and maintaining one common enterprise standard across hubs.
