As a Machine Learning Engineer, you will work with clients in the financial sector to design, develop, and operationalize data-driven solutions. Your responsibilities will span the full lifecycle of ML initiatives spanning from early experimentation to deployment, monitoring, and continuous optimization.

Projects may involve automated or agentic analytical pipelines, forecasting models, anomaly-detection systems, or other statistical and machine learning solutions. Much of the work will run within cloud-based environments, where you will ensure that pipelines and models are reliable, scalable, and aligned with the high standards typical in a regulated environment.

You will collaborate with software engineers, infrastructure and domain teams to integrate solutions smoothly into enterprise ecosystems. Strong interpersonal skills will support effective communication with stakeholders both onsite and remotely.

Required competence and attitudes

• Master or PhD degree in Computer Science, Mathematics, Physics, Informatics, Engineering, or equivalent discipline.
• Strong programming skills (Python, SQL, …) and familiarity with ML libraries (TensorFlow, PyTorch, Scikit-learn, etc.).
• Solid understanding of ML principles, statistical modeling, and modern data-processing techniques.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP) and with deploying ML systems in scalable, production-grade environments.
• Experience with data visualization tools (e.g., Tableau, Matplotlib).
• Familiarity with Git-based development workflows (GitHub/GitLab/Bitbucket).

Desirable

• Familiarity with cloud ML services and hybrid cloud architectures (e.g., AWS SageMaker, Azure ML, Vertex AI).
• Knowledge of deployment and orchestration tools (Docker, Kubernetes, CI/CD).
• Understanding of data systems and architectures (e.g., PostgreSQL, distributed data frameworks).
• Proficiency in Italian or German; French is a plus.

We offer

• Full-time permanent contract.
• Fast career development, attractive compensation.
• Stimulating scientific context and informal environment.
• Training, tutoring, and close relationship with cutting-edge research teams.