MLOps Engineer

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. 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. German is a plus.