Senior Software Engineer with AI/ML Experience
RUAG AG
Bern
Key information
- Publication date:03 October 2025
- Workload:100%
- Place of work:Bern
What you can achieve
- Development and maintenance of software solutions for integrating ML models into products and platforms
- Design and implementation of scalable APIs and services for ML models
- Building data and model pipelines with a focus on reliability, security, and efficiency
- Collaboration with data scientists and ML engineers to implement models into production systems
- Application of best practices in software quality (clean code, testing, CI/CD, code reviews)
- Monitoring, debugging, and optimizing existing ML-powered applications
What you bring
- Bachelor's or Master's degree in computer science or a related field
- Several years of experience as a software developer, e.g., in Python, Java, or C++
- Experience with containerization and orchestration (Docker, Kubernetes, OpenShift)
- Team-oriented working style with a focus on collaboration and adaptability
- Very good German and English skills, both written and spoken
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 IT at RUAG, you have the opportunity to cover the entire ICT landscape from development to maintenance and contribute your expertise to Switzerland's security.
- Bachelor's or Master's degree in computer science or a related field
- Several years of experience as a software developer, e.g., in Python, Java, or C++
- Experience with containerization and orchestration (Docker, Kubernetes, OpenShift)
- Team-oriented working style with a focus on collaboration and adaptability
- Very good German and English skills, both written and spoken
- Development and maintenance of software solutions for integrating ML models into products and platforms
- Design and implementation of scalable APIs and services for ML models
- Building data and model pipelines with a focus on reliability, security, and efficiency
- Collaboration with data scientists and ML engineers to implement models into production systems
- Application of best practices in software quality (clean code, testing, CI/CD, code reviews)
- Monitoring, debugging, and optimizing existing ML-powered applications
