Industrial ML Engineer - AI/ML Ops for Waste Tech

Kanadevia Inova AG; Zürich; Vor Ort; CHF 120.000 - 160.000; Vollzeit

Zusammenfassung

Kanadevia Inova AG in Switzerland seeks a skilled ML Engineer to industrialise AI/ML solutions and build scalable, production-ready applications. You will design and operate end-to-end MLOps pipelines, from data training to deployment and monitoring, while collaborating closely with data scientists, engineers, product managers and business stakeholders to deliver impactful AI solutions.

You will ensure reliability, security, and governance across the ML lifecycle, monitor model performance,

Qualifikationen

• MSc in Computer Science or STEM with a strong CS focus.
• Strong Python programming skills with ML frameworks (PyTorch, TensorFlow or Scikit-learn).
• Solid understanding of MLOps: deployment, versioning, monitoring and lifecycle management.
• Experience with cloud platforms (Azure preferred), Docker, APIs, databases and distributed systems.

Aufgaben

• Industrialise and operate AI/ML solutions by transforming prototypes into scalable, production-ready applications.
• Design, implement, and maintain end-to-end MLOps pipelines covering training, validation, deployment, monitoring and retraining.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, Product Managers and business stakeholders to deliver AI solutions.
• Ensure reliability, security, compliance, data integrity, performance and governance across the ML lifecycle.
• Monitor and optimise model performance, troubleshoot production issues and mentor less experienced colleagues.

Jobbeschreibung

Kanadevia Inova AG in Switzerland seeks a skilled ML Engineer to industrialise AI/ML solutions and build scalable, production-ready applications. You will design and operate end-to-end MLOps pipelines, from data training to deployment and monitoring, while collaborating closely with data scientists, engineers, product managers and business stakeholders to deliver impactful AI solutions.

You will ensure reliability, security, and governance across the ML lifecycle, monitor model performance,