AI and Data Engineering lead

Job TitleAI and Data Engineering lead
Job Description
About Vanderlande
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.
Position Overview
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.
Key Responsibilities
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 engineering disciplines and domains.
Partner with universities, research institutions, technology providers and selected customers to accelerate capability development.
Capability Building and Adoption
Build specialist AI knowledge in deep technical functions
Build AI capability across Vanderlande’s engineering and technical population.
Introduce AI engineering tools alongside the standards, training and workflow changes required for effective use.
Measure adoption through changed ways of working and engineering outcomes, not only tool usage or training completion.
Governance and Responsible AI
Ensure AI-enabled engineering solutions comply with enterprise AI standards and governance.
Embed security, intellectual property protection, data sovereignty, traceability and responsible AI practices.
Establish appropriate evaluation and validation methods for AI-generated engineering outputs.
Partner with ICT, Data, Security, Legal, Quality and the AEC to move solutions safely from experimentation to operational use.
What Will Success Look Like?
During the first year, you will:
establish the Technology AI and data strategy and roadmap;
identify and quantify the main constraints in the end-to-end engineering value stream;
accelerate the Engineering Configurator toward integration with relevant engineering systems;
launch priority AI-enabled engineering initiatives with measurable cycle-time, throughput or quality improvements;
establish multidisciplinary fusion teams;
introduce reusable AI architecture and engineering data patterns;
scale AI-assisted working practices across selected engineering teams;
implement a performance dashboard covering time to market, engineering throughput, adoption, reuse and quality.
Over time, AI and data will become embedded in Vanderlande’s standard engineering lifecycle, contributing to shorter development cycles, improved engineering quality and faster delivery of customer solutions.
What Do We Ask From You?
You are an entrepreneurial and execution-oriented technology leader who understands that AI transformation requires a redesign of engineering work, not simply the introduction of new tools.
You combine strategic vision with a strong understanding of engineering, AI, data and organizational change. You can operate effectively with engineers, architects, product leaders and senior executives in a global, matrixed environment.