Machine Learning Engineer

On-siteSeniorML Engineerposted 2w ago

✦Role summaryAI-generated

This role focuses on transforming AI prototypes into production-ready applications and building end-to-end MLOps pipelines in Zürich. Candidates will collaborate with cross-functional teams, ensure compliance and performance, and mentor junior engineers while monitoring model health.

Skills required

Python MLOps Docker Kubernetes TensorFlow PyTorch scikit-learn Monitoring AI Machine learning Software engineering Cloud computing Azure Distributed systems Data engineering Time series Computer vision

About this role

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.