Machine Learning Engineer
Pubblicato il 21-08-2026 - Tenova in Varese
Overview
Tenova, a Techint Group company, is a worldwide partner for sustainable, innovative and reliable solutions in the metals and mining industries. Tenova leverages a workforce of over 2,500 professionals located in 18 countries across 5 continents, who design technologies and develop services that help companies reduce costs, save energy, limit environmental impact and improve working conditions.
Your role in Tenova: Machine Learning Engineer
Responsibilities
- Work across the end-to-end machine learning lifecycle, from problem definition and data preparation to model development, deployment, and monitoring
- Build, together with our Software engineers, AI based applications deployed on industrial machinery to boost production efficiency, reduce unplanned downtime and enable predictive maintenance
- Work with colleagues from different Product Lines to help them create and bring innovative solutions to the market
What we ask for
- STEM Degree (Science, Technology, Engineering and Mathematics)
- In depth experience with Python and knowledge of ML libraries (e.g. pandas, sklearn,
keras/pytorch)
- Hands-on experience developing and deploying machine learning models
- Good Knowledge of English (written and spoken)
Nice to have
- Experience with building and deploying LLM models
- Experience with ML Computer Vision models
- Knowledge of Agile methodology and SCRUM
- Knowledge of Microsoft Azure. We use Synapse and ML Workspace
What we offer
- The opportunity to work on innovative AI-driven projects with real business impact.
- A dynamic and international environment where ideas are valued
- Continuous learning (training, exposure to new technologies, cross-team collaboration)
- The applicable collective labor agreement is CCNL per Industria Metalmeccanica Privata e della Installazione di Impianti
- Hybrid working model (6-8 days)
- 14th-month salary
- Performance premio linked to business results
- Performance Management: continuous development process with multi-