AI Engineer (PyTorch, MLOps) — Cloud Deployments

Twine; Schweiz; Vor Ort; CHF 120.000 - 180.000; Vollzeit

Zusammenfassung

Twine in Switzerland seeks an experienced AI/ML Engineer to design, train, deploy and optimize deep learning models for production. You will build scalable data pipelines, integrate models into cloud services, and monitor performance.

Collaborate with software, product and data teams to deliver end-to-end AI solutions and implement MLOps for CI/CD. Responsibilities include troubleshooting, ensuring reliability and accelerating deployment speed for mission-critical applications in a fast-paced

Qualifikationen

• 3+ years of experience building and deploying ML models in production.
• Proficiency in Python and at least one of C++ or Java.
• Hands-on with PyTorch, TensorFlow, or Scikit-learn.
• Strong Docker, Kubernetes and cloud platform experience (AWS or GCP).
• Experience designing data pipelines and APIs.
• Knowledge of MLOps, CI/CD and model monitoring.
• Advanced SQL skills and excellent English communication.

Aufgaben

• Design, train, deploy and optimize DL models for production.
• Build scalable data processing pipelines for training and inference.
• Integrate ML models into cloud services and APIs.
• Monitor model performance and troubleshoot in production.
• Collaborate with software, product and data teams on end-to-end AI solutions.
• Apply MLOps best practices for CI/CD in production environments.
• Improve speed, reliability and scalability of mission-critical apps.

Kenntnisse

Machine learning in prod

Python

C++ or Java

PyTorch / TensorFlow / Scikit-learn

Docker / Kubernetes

Cloud platforms (AWS or GCP)

SQL

English communication

Jobbeschreibung

Twine in Switzerland seeks an experienced AI/ML Engineer to design, train, deploy and optimize deep learning models for production. You will build scalable data pipelines, integrate models into cloud services, and monitor performance.

Collaborate with software, product and data teams to deliver end-to-end AI solutions and implement MLOps for CI/CD. Responsibilities include troubleshooting, ensuring reliability and accelerating deployment speed for mission-critical applications in a fast-paced