WOFÜR WIR SIE SUCHEN

Deploy and operate machine learning models in standardized and scalable production environments
Implement robust traffic handling and autoscaling mechanisms to ensure reliable and efficient model serving
Design and manage advanced deployment strategies that reduce risk during model releases and updates
Optimize inference performance by improving hardware utilization and minimizing latency for production workloads
Package and release machine learning models in fully containerized and version-controlled environments
Ensure reproducibility and integrity of model artifacts across development, testing, and production systems
Establish comprehensive monitoring for inference performance, data quality, and model behavior in production
Support governance and compliance by maintaining traceable release artifacts, validation results, and audit-ready documentation
Successfully completed studies in computer science, machine learning, artificial intelligence, software engineering, or a comparable qualification.
Several years of professional experience in machine learning operations, platform engineering, or DevOps environments supporting machine learning systems.
Strong experience deploying and operating machine learning models in production environments.
Solid knowledge of containerized workloads and orchestration platforms for scalable model deployment.
Experience working with model versioning systems and managing machine learning artifacts across environments.
Practical experience optimizing machine learning models for efficient inference and hardware utilization.
Structured and analytical approach to solving complex operational challenges in machine learning systems.
Strong collaboration and communication skills when working with engineering, data science, and platform teams.

An unserem Standort in Bremen bieten wir Ihnen:

Betriebliche Altersvorsorge
Aktienkaufprogramm
30 Urlaubstage
Zugang zu den Corporate Benefits
Deutschlandticket
Umzugsunterstützung
VIVA Familienservice
Individuelle und vielfältige externe sowie interne Weiterentwicklungsmöglichkeiten u.a. in der Rheinmetall Academy
Professioneller Einarbeitungsprozess begleitet durch ein digitales Onboarding

MLOps Engineer (m/f/d)