Supply Chain & Engineering AI internship

Job Description

Are you looking for an internship where you can:

• Combine consulting, engineering, and applied AI
• Work in a fast-moving environment
• Support leading industrial companies on real transformation projects
• Work on a research assignment that covers strategy, process improvement, and AI technology implementation

Then an AI-focused role within Supply Chain & Engineering (SC&E) could be a great fit for you!

The team

SC&E is a diverse and dynamic team working across Engineering and Manufacturing.

You will join the AI Engineering team, which focuses on next-generation R&D and on reinventing the future of products and the end-to-end product journey, from initial design to managing Product Lifecycle Systems, their sustainable footprint, and the ultimate retiring and replacement. The team specializes in companies focused on product development and is mainly active in Research & Development departments. Here, industry-leading clients are supported in optimizing business processes and efficiency through AI.

Daily tasks

As an intern at SC&E, you will work with colleagues to conduct thesis research and an assignment in one of the relevant research areas listed below. Your thesis will focus on challenges or opportunities seen at clients, with the goal of defining new AI-driven business solutions or opportunities based on input from experts. Depending on the assignment, you may prototype and deploy working AI solutions, from data pipelines and model experiments to proof-of-concept applications. During your internship, you'll connect with many SC&E colleagues and gain insight into the world of strategy and consulting.

Research

Research groups:

• Defining R&D and AI strategies: Helping clients shape their long-term vision and roadmap for R&D and AI-driven digital transformation.
• Improving engineering and business processes: Analyzing current ways of working and redesigning product development processes through workshops, interviews, and practical improvements, including where generative AI and automation can accelerate them.
• Implementing AI solutions: Supporting the rollout of technologies such as Product Lifecycle Management (PLM) platforms and AI-enabled solutions like RAG systems, LLM orchestration, and computer vision.
• Driving sustainability initiatives: Setting up frameworks, processes, and data-driven insights that help companies reach their sustainability goals.