Curricular Internship
Pubblicato il 07-10-2026 - HypoVereinsbank in Italia
Apply as Curricular Internship - Group Data & Artificial Intelligence
General Information
Job ID
21211
Company
UniCredit S.p.A
Competence Line
Digital
Country
Italy
City
MILANO
Job Type
Working Students, Interns & others
Functional area
Digital & Data
Working time model
Full-time
Contact
Salary period
Monthly
Salary Currency
EUR
Base Salary (gross)
1.000
We are the Artificial Intelligence department within the Data & AI team under the Digital Department of UniCredit S.p.A.
Our team, Group AI Products, is responsible for designing, developing and scaling up analytical and AI-driven products while identifying innovation opportunities for the Group.
Aligned with UniCredit's Group Data & Artificial Intelligence mission, we identify and explore innovation opportunities through collaborations and knowledge-sharing initiatives, we design foundational assets on unified data platform and cloud technologies, and develop advanced data products leveraging AI, data science, and cognitive computing.
What you'll be doing
- Assist in deploying AI models from development to production and implementing scalable MLOps standards.
- Collaborate with Data Scientists to industrialize models and optimize inference pipelines.
- Explore and enhance Generative AI technologies (e.g., GPT-4, Bard) through prompt engineering techniques.
- Support end-to-end management of AI-driven digital products, focusing on innovation and customer experience.
- Contribute to cross-functional projects leveraging AI for banking solutions and strategic decision-making.
- Prepare top-management reports and maintain project documentation for impactful business initiatives
What you'll need to succeed
- Bachelor’s/master’s degree in STEM fields (e.g., Engineering, Computer Science, Physics, Mathematics, Statistics).
- Basic knowledge of machine learning workflows, AI and programming languages (Python is a plus).
- Interest in Generative AI and familiarity with Large Language Models (LLMs) and MLOps principles; expe