Senior ML Ops Engineer (m/f/d)

About the role

As a Senior MLOps Engineer, you will take ownership of designing, building, and maintaining the infrastructure that powers our Generative AI framework. You will architect scalable, secure ML environments across development, testing, and production, while automating the entire ML lifecycle—from data ingestion and model training to deployment and monitoring. In this role, you will lead the design of CI/CD pipelines for ML workflows, ensuring reliability, reproducibility, and seamless integration of new models. You will implement advanced monitoring and observability systems to track performance, detect data drift, and maintain infrastructure health. Your expertise will also drive the development of robust tooling for model development, training, serving, and lifecycle management, enabling our teams to bring cutting-edge AI solutions into production at scale. 

Your Responsibilities

• Design, build, and maintain scalable and secure ML infrastructure across development, testing, and production environments
• Automate andoptimizethe ML lifecycle
• Architect and manage the continuous integration and deployment pipelines and release processes using tools such as Kubeflow,MLflow, SageMaker, or custom Kubernetes solutions
• Implement monitoring systems for data drift, model performance, and infrastructure health
• Develop Tooling: Build and enhance ML engineering tooling for Model Development, Model Workbench, Model Training, Model Monitoring, and Model serving
• Work closely with data scientists and ML engineers to ensure reproducibility, scalability, and production-readiness of models
• Design and maintain pipelines for feature extraction, transformation, and storage using tools like Feature Store or custom solutions
• Ensure data quality, consistency, and lineage throughout the ML pipeline
• Ensure responsible use of data, model explainability, and auditability in line with organizational and legal standards

Essential Skills

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 5+ years in DevOps/software engineering/infrastructure, with 2–3 years in MLOps or production ML systems
• Proficiency in Python, Bash or Go is a plus
• Model versioning, monitoring, and lifecycle management
• Strong with ML tooling (MLflow, Kubeflow, Airflow, SageMaker, etc.)
• Expertise in containerization & orchestration (Docker, Kubernetes)
• Infrastructure-as-Code (Terraform; Pulumia plus)and CI/CD pipelines (GitLab CI, Jenkins, ArgoCD)
• Cloud platforms (AWS, GCP, or Azure)
• Proventrack record with GPU-accelerated systems at scale
• Experience with cluster/cloud compute technologies (SLURM, Lustre, k8s)
• Benchmarking expertise(software & hardware)
• Strong problem-solving/debugging with the ability to lead infra initiatives independently
• Excellent communication and cross-functional collaboration

What we offer

• A dynamic, highly qualified, and diverse team in which your contributions are reflected directly in our products and used by our international customer base
• Flat hierarchies and short decision-making processes
• Exciting and varying tasks for our product portfolio
• Excellent working environment, modern office space, and flexible working hours with the option of mobile working