AI Software Engineer – Intern
Pubblicato il 18-09-2026 - Nokia in Vimercate
Job Description
We are looking for a motivated AI Software Engineer Intern to join our team and contribute to Artificial Intelligence projects supporting our engineering and R&D; activities. You will work alongside experienced software and AI engineers on real-world applications involving Generative AI, Large Language Models (LLMs) and enterprise software development. This position is intended for a student with good programming fundamentals who wants to develop practical experience in AI software engineering. Rather than focusing only on theoretical AI or Machine Learning, you will learn how modern AI capabilities can be integrated into actual software applications and used to solve engineering and business problems. We do not expect a student to already be an AI expert. We are primarily looking for someone with solid programming foundations, technical curiosity and a genuine interest in Artificial Intelligence. The ideal candidate enjoys building and understanding software, is willing to experiment and learn,
and is interested in discovering how Generative AI can be used in real engineering applications. The position will provide practical experience across different aspects of AI software development, from initial experimentation and prototyping to integration with existing applications and enterprise data. Over time, the successful candidate will be encouraged to become increasingly autonomous and take responsibility for more complex AI development tasks.
How You Will Contribute And What You Will Learn
Contribute to AI Application Development
- Participate in the development of applications and services leveraging Large Language Models and Generative AI.
- Build prototypes to explore and validate new AI use cases.
- Experiment with different models and prompting approaches.
- Integrate AI models into applications using APIs and SDKs.
- Contribute to solutions using internal documents and enterprise data.
- Participate in the development of Retrieval-Augmented Ge