Internship: Development of a large language models Based AI Agent for Storage System Configurations

Internship: Development of a Large Language Models Based AI Agent for Storage System Configurations

Start date: February 2026

Assignment duration: 6 months

Desired study: Software Engineering, Data Science, Computer Engineering

Language: Dutch/English

Description of Assignment

Adapto systems are modular storage solutions developed by Vanderlande, used in both Airport and Warehousing environments. The configuration of these systems depends on a wide range of factors, such as the total footprint, the cost of individual components, and the required system throughput.

Designing and configuring these systems currently requires significant expertise and experience, as well as close collaboration between multiple departments. Over the years, Vanderlande has built up a large database of project reports, simulation results, and other relevant documentation based on real-world implementations of Adapto systems.

The goal of this internship is to develop an AI agent powered by a large language model (LLM) that can leverage this existing knowledge base to assist with configuration-related queries. For example, the agent should be able to respond to questions such as:

“Given these lift and shuttle parameters, how should I allocate the shuttles to achieve the required throughput?”

This AI agent should help streamline the configuration process, reduce reliance on expert knowledge, and make valuable insights from past projects more accessible to engineers and stakeholders.

Department Description

The Digital Twin Suite (DTS) department at Vanderlande is responsible for developing Emulation and Simulation software used across multiple departments within the company. This software plays a key role throughout various project phases, from the initial sales stage to detailed engineering, and is capable of modelling both Airport and Warehousing systems.

DTS consists of several specialized teams, each focusing on different components of the suite:

• Saga: the high-level simulation platform
• Care: the low-level emulation engine
• Cloud Environment team, which is working on running these software modules remotely

You will primarily work within the Saga team, contributing to ongoing simulation development. However, close collaboration with the Care team and the Simulation department is expected, offering the student a broader perspective on the full digital twin pipeline and how various technologies come together to simulate complex logistical systems.

Tasks/Responsibilities

• Research and gain a basic understanding of how Adapto systems function within Vanderlande.
• Collect relevant internal information (e.g. reports, codebase, Git tickets).
• Develop a local LLM-based agent trained on this data.
• Create a setup to deploy the agent in both the online Cloud environment and local Simulation models.
• Conduct a user experience study across departments to evaluate the agent’s usability and effectiveness.

Your Profile

• Familiarity with machine learning frameworks.
• Understanding of how LLM (large language models) work.
• Experience with Python and REST API.
• Basic understanding of Java.
• Mandatory enrolment to a Dutch Education System & resident of The Netherlands.

For more information, contact us by e-mail: internship@vanderlande.com.