RUAG AG
Bern
Last month
Senior Machine Learning Engineer
- Publication date:04 October 2025
- Workload:100%
- Place of work:Bern
About the job
What you can achieve
- Development, training, and deployment of machine learning models for production operation
- Building and maintaining end-to-end ML pipelines (data preparation, feature engineering, model training, monitoring, etc.)
- Ensuring transparency, traceability, and scalability through best practices (CI/CD, MLOps, testing)
What you bring
- Bachelor's or Master's degree in Computer Science or a related field
- Several years of professional experience in the machine learning/MLOps/DevOps environment
- Experience as a pre-sales or software engineer is an advantage
- Strong knowledge in software engineering with Python, TensorFlow, PyTorch, and other ML frameworks, as well as Docker, Kubernetes, APIs and testing, CI/CD, version control with Git, etc.
- Team-oriented working style with a focus on collaboration and adaptability
- Very good English and/or German skills for collaboration with team members
Salary and benefits
"Nerd" is not an insult but a status symbol? You know more about computers and networks than Bill Gates? Then we want you on our team. In the IT department of RUAG, you have the opportunity to cover the entire ICT landscape from development to maintenance and contribute your expertise to the security of Switzerland.
- Bachelor's or Master's degree in Computer Science or a related field
- Several years of professional experience in the machine learning/MLOps/DevOps environment
- Experience as a pre-sales or software engineer is an advantage
- Strong knowledge in software engineering with Python, TensorFlow, PyTorch, and other ML frameworks, as well as Docker, Kubernetes, APIs and testing, CI/CD, version control with Git, etc.
- Team-oriented working style with a focus on collaboration and adaptability
- Very good English and/or German skills for collaboration with team members
- Development, training, and deployment of machine learning models for production operation
- Building and maintaining end-to-end ML pipelines (data preparation, feature engineering, model training, monitoring, etc.)
- Ensuring transparency, traceability, and scalability through best practices (CI/CD, MLOps, testing)
