(Senior) Machine Learning Operations Engineer (m/f/d) - REF97172N

Continental is a leading tire manufacturer and industry specialist. Founded in 1871, the company generated sales of €19.7 billion in 2025 and currently employs around 76,000 people in 54 countries and markets. Tire solutions from the Tires group sector make mobility safer, smarter, and more sustainable. Its premium portfolio encompasses car, truck, bus, two-wheel, and specialty tires as well as smart solutions and services for fleets and tire retailers. Continental has been delivering top performance for more than 150 years and is one of the world’s largest tire manufacturers. In fiscal 2025, the Tires group sector generated sales of 13.8 billion euros. Continental's tire division currently employs around 53,000 people worldwide and has 19 production and 16 development sites. Join Continental’s Digital Solutions Department as an MLOps Engineer In Digital Solutions, we drive innovation for connected mobility and deliver cutting-edge digital products to our global fleet customers. As an MLOps Engineer, you will be at the forefront of transforming machine learning into scalable, production-ready solutions. You will design, deploy, and maintain robust ML pipelines , ensuring seamless integration of models into production environments and optimizing their performance at scale. Working closely with Data Scientists, Data Engineers, and cross-functional teams , you will enable AI-driven insights that power smarter, safer, and more efficient mobility worldwide. This position is intended to be filled on a full-time basis. However, applications for part-time employment will be considered in accordance with business needs. Main responsibilities : Design and manage CI/CD pipelines for ML models from development to production Build and maintain scalable ML infrastructure on cloud platforms using infrastructure-as-code Automate deployment, monitoring, and rollback processes for reliability and reproducibility Implement monitoring and feedback loops for model performance and contin