MLOps Engineer

February 7

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COMPREDICT

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COMPREDICT is a company specializing in virtual sensor technology to transform vehicle data into actionable insights. Through purely software-based solutions, COMPREDICT enhances existing hardware with virtual sensors, enabling new measurement capabilities, vehicle health, and usage monitoring. Their platform is designed for vehicle OEMs, tier 1 suppliers, and fleet solution providers, offering predictive maintenance technology and optimizing component lifecycle. The company focuses on reducing the need for physical sensors, saving costs and improving the development process using AI and machine learning. With strategic partnerships, such as those with Renault and UTAC, COMPREDICT is at the forefront of advancing mobility through innovative data-driven solutions.

Machine Learning • Predictive Maintenance • Big Data • Automotive Engineering • Virtual Sensors

11 - 50 employees

🤖 Artificial Intelligence

☁️ SaaS

🔥 Funding within the last year

💰 $15M Series B on 2024-06

📋 Description

• 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, such as predicting tire pressure without traditional sensors and assessing a car's battery health to proactively identify potential issues. • 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 includes MLOps Pipeline Development and Optimization, Model Comparison and Validation, Collaboration, and Documentation and Knowledge Sharing.

🎯 Requirements

• Proficient in modern DevOps practices and microservice architectures. • Experience with CI/CD pipelines and automation tools. • Expertise in Kubernetes and containerization technologies (e.g., Docker). • Familiarity with platforms such as KubeFlow, MLflow, or equivalent. • Hands-on experience with AWS or other cloud service providers. • Understanding of time series modeling and its data requirements. • Knowledge of deep learning concepts 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.