Master Thesis / Internship : Leverage AI to optimize presales activities

Description
Process automation – Some processes are complex as they require the intervention of several coordinated actors and a set of different activities that combined all together provide a valuable output. The objective of this internship is to implement an application backed by LLMs and agents, that support the work of teams in specific pre-sale process activities.
Improved presales efficiency and quality – The application should improve the efficiency of the process by delivering key components faster, with less human efforts. The system should also help to leverage experience, explore more solution scenarios, and assist collecting and presenting relevant information.
 

Objectives

Implement a module as part of a larger application – The objective is to deliver a specific application module that supports the different LLM agents in the target solution. 
Test the module and confirm its relevance – The output produced by the module should be tested against real cases to evaluate its performance based on the relevance of the results. End users should be involved in the rating of the implemented application.
The results will be presented internally to multiple stakeholders. An article should also be published on the web to present the results of the internship.
 

Our offer

• A dynamic work and collaborative environment with a highly motivated multi-cultural and international sites team
• The chance to make a difference in peoples’ life by building innovative solutions
• Various internal coding events (Hackathon, Brownbags), see our technical blog
• Monthly After-Works organized per locations

Skills required

• Knowledge of NLP and techniques for interacting with LLMs, knowledge in app development.
• Practical software development skills in one or more programming languages -ex: Python, Java
• Nice to have: knowledge of commonly used services of Azure or AWS, knowledge of foundational models, knowledge of Open WebUI
   

This internship starts in February 2026. 

Applications must include your most recent academic transcripts (grades); applications without transcripts will not be considered.