Senior MLOps Engineer

Quick Facts

• Role: (Senior) MLOps Engineer

Description

Build and operate an MLOps platform that enables taking internally developed or open-source ML models into an end-to-end autonomy stack. You will design training, evaluation, and deployment pipelines that run reliably in multi-cloud environments and on a secured internal cluster, while ensuring full reproducibility and traceability across the model lifecycle.

Responsibilities

• Create standards to adopt, evaluate, and integrate internal or open-source models into the autonomy stack
• Help shape and build the MLOps platform using components such as Kubernetes, MLflow, and GitLab
• Develop pipelines for training, evaluation, and deployment for multi-cloud and a secured internal cluster
• Ensure reproducibility and traceability from data and model versioning through experiment tracking to a model registry
• Partner with DevOps and simulation teams to measure model quality in a systematic and comparable way
• Support setup of model optimization pipelines to improve inference performance
• Collaborate with ML, software/system engineering, and infrastructure operations teams to bring the ML-in-production perspective into all teams
• Integrate ML trainings and resulting models into software- and hardware-in-the-loop environments while maintaining reproducibility, scalability, and developer experience
• Contribute to transforming ML models across domains (from perception to decision-making) into deployable components for autonomous UAVs

Requirements

• STEM degree (e.g., Computer Science, Mathematics, Electrical Engineering, Aerospace Engineering, Artificial Intelligence)
• Experience in MLOps or ML Engineering with models and platforms actively used for production systems
• Strong ML understanding (how models are trained, evaluated, and improved) and hands-on PyTorch experience, including common formats like ONNX
• Very good Python skills; interest in clean, maintainable software; C++ knowledge is a plus
• Experience with Kubernetes, CI/CD, and MLOps tools such as GitLab, Kubeflow, MLflow, KServe, and artifact registries
• (Preferred) Experience with at least one of: edge inference model optimization (e.g., TensorRT, quantization), hardware-in-the-loop, simulation/synthetic data, distributed GPU cluster training
• System-oriented thinking
• Team spirit, clear communication, and interest in shaping a platform being built
• Fluency in German and English
• Awareness of compliance risks and integrity

Benefits

• Permanent full-time role
• Attractive compensation, flexible working hours, and a hybrid work model (up to 40% mobile)
• Opportunities for professional growth within the Airbus group
• Support for personal development with tailored learning and development solutions
• Engaged team and inspiring environment to help build autonomous systems of the future