AI and Data Engineering lead

Job Description

Vanderlande is a global leader in logistics automation, supporting leading airports, retailers and e-commerce organizations worldwide. Our Technology Business Unit develops the equipment, software, architecture and integration capabilities that enable reliable, scalable and cost-effective logistics solutions. Working at Vanderlande offers a unique opportunity to make a global impact. You will work in an international and innovative environment, collaborating with engineering teams, technology leaders and business domains to shape the future of logistics automation.

The primary mission of the AI & Data Engineering department is to accelerate Vanderlande's transition towards an AI-native engineering organization, reducing time-to-market by transforming the end-to-end engineering value stream through AI, data and intelligent automation. The role focuses on transforming the end-to-end engineering way of working, from customer requirements and system design through configuration, development, testing and deployment. By embedding AI, intelligent automation and data-driven decision-making into engineering processes, the role will help shorten development cycles, increase engineering productivity, improve quality and accelerate time to market.

The AI & Data department will develop reusable AI-enabled engineering capabilities, data products, reference architectures, tools and working methods that can be adopted across Vanderlande’s engineering disciplines and domains. This role is specifically accountable for using AI and data to transform Technology and the end-to-end engineering lifecycle. The AEC remains the enterprise front door, while the AI & Data Department creates and scales the specialist engineering capabilities needed within Technology.

AI & Data Strategy for Technology

Develop and execute the AI and data strategy for the Technology Business Unit. Translate Technology priorities into a focused portfolio of AI and data initiatives. Align the roadmap with domain product strategies, platform-based development and the Engineering-to-Order to Configure-to-Order transformation. Own the Technology AI and data portfolio, roadmap and prioritization process. Identify opportunities where AI and data can reduce engineering lead times, remove rework and improve development quality.

Ensure investments deliver measurable improvements in engineering throughput and time to market.

AI in Engineering – E2E Transformation

Redesign engineering workflows around AI-enabled capabilities rather than adding individual tools to existing processes. Reduce handoffs, waiting time, repeated data entry and fragmented engineering information. Establish reusable AI-enabled engineering patterns across equipment, software, systems, architecture and integration disciplines. Connect requirements, engineering decisions, models, code, tests and lifecycle data to create faster feedback loops. Lead the development and scaling of the Engineering Configurator and other priority engineering AI solutions.

Create engineering data products and knowledge structures that make technical information accessible, reusable and suitable for AI. Integrate AI capabilities with relevant engineering environments, including BOM, CAD, PLM, ALM and software development platforms.

Fusion Teams and Ways of Working

Fusion teams are particularly important for AI solutions because engineering, data, architecture, risk and users need to remain involved throughout the solution lifecycle. Establish multidisciplinary teams that combine engineering, AI, data, architecture, software, product and change expertise. Build communities of practice across…