Machine Learning Engineer

Kanadevia Inova, Zürich, Suisse

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Publiée le 24 septembre 2026 · première apparition dans nos relevés le 25 septembre 2026.

Annonce stable : vue pour la première fois le 25 septembre 2026, sans republication anormale.

Cette annonce n'affiche pas de salaire, alors que 1 % des annonces ouvertes du même secteur dans ce pays (Suisse) le font.

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## Company Description ## Welcome to Kanadevia Inova, a global innovation leader in the waste infrastructure space, where we believe in creating a sustainable future through technology and innovation. **Transforming Waste into Value** At Kanadevia Inova, we pride ourselves on being at the forefront of waste-to-X technology. We are not just waste managers; we are creators of value from what communities discard. Your role at Kanadevia Inova directly contributes to turning something once considered useless - waste - into something invaluable: energy, heat, hydrogen, fertilizer, and beyond. ## Job Description ## * 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 closely 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 throughout the machine learning lifecycle. * Monitor and optimise model performance, troubleshoot production issues, and support continuous improvement while mentoring less experienced colleagues. ## Qualifications ## * MSc in Computer Science or a STEM field with a strong computer science focus, plus experience working in agile software development environments. * Strong Python programming skills and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. * Solid understanding of MLOps practices, including model deployment, versioning, monitoring, and lifecycle management in production environments. * Experience with cloud platforms (preferably Azure), containerisation technologies (Docker), APIs, databases, and distributed systems. * Knowledge of data engineering and data processing frameworks; experience with industrial IoT, time-series data, computer vision, or Physics-AI solutions is advantageous. ## Additional Information ## For HR agencies: Please note that we do not accept applications coming from agencies. Thank you.

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