MLOps Engineer (m/f/d)

Das ist der Job

German is a plus.

Darum lohnt es sich

As the number of deployed models grows, your expertise will be pivotal in managing model comparisons and maintaining performance standards. **Your Role in More Detail:** **MLOps Pipeline Development and Optimization:** - Design and maintain scalable pipelines for deploying machine learning models whether in-cloud or in-vehicle. - Ensure models are securely integrated into production environments with minimal latency. - Implement monitoring systems to track model performance and flag issues. **Model Comparison and Validation:** - Develop methods to evaluate and compare the performance of different models. - Automate processes for validating model accuracy and consistency in production. **Collaboration:** - Work closely with data scientists, developers, and stakeholders to understand their needs and provide tailored solutions. - Effectively communicate technical processes and outcomes to both technical and non-technical audiences. **Documentation and Knowledge Sharing:** - Create comprehensive documentation for processes, pipelines, and workflows. - Provide training and guidance to team members on MLOps best practices. ## Your profile - At least 2 years working experiences in modern DevOps practices and microservice architecture. - Expertise in Kubernetes and containerization technologies. - Hands-on experience with platforms such as KubeFlow, Kserve, or equivalent. - Experience in ML Experimentation and registry platforms such as W&B or MLFLow. - Understanding of time series modeling and its data requirements. - Familiar with ML/NN frameworks. - Familiar with AWS or other cloud service providers is a plus. - Strong ability to collaborate with cross-functional teams, including data scientists, engineers, and clients. - Clear and concise in verbal and written communication, with excellent documentation skills. - Fluent in both written and spoken English. ## Your mission As an MLOps Engineer, you will play a critical role in ensuring our machine learning models transition seamlessly from research to production.

These models analyze car data over time to generate actionable insights, a couple examples of COMPREDICT portfolio: - Predicting tire pressure without traditional sensors. - Predict the correct angle of vehicle’s front headlights to provide optimal visibility for the driver without blinding oncoming traffic.

Your primary responsibility is to design, implement, and maintain a robust, efficient, and secure pipeline that supports the entire lifecycle of machine learning models, from development to deployment and monitoring.