Machine Learning Infrastructure Engineer
As a Machine Learning Infrastructure Engineer, you will be responsible for designing, deploying, and maintaining scalable, resilient, and reproducible machine learning infrastructure. You will play a crucial role in operationalizing ML solutions in close partnership with data scientists and engineers.
AUTO1 Group Technology drives innovation in the used car market across Europe. You will work at the intersection of software engineering, data science, and DevOps, helping bring state-of-the-art ML models safely into production.
Key Responsibilities:
* Own the ML lifecycle: Implement robust, containerized, and reproducible pipelines for model training, evaluation, and deployment across both batch and real-time settings.
* Operationalize models at scale: Build and manage ML services, APIs, and model serving infrastructure using tools like MLflow, Amazon SageMaker, and Feature Store.
* Automate and monitor: Set up and maintain monitoring, observability, and alerting systems to ensure high availability and performance.
* Accelerate experimentation: Develop and maintain internal libraries, templates, and platform tooling to improve reproducibility and simplify deployment workflows for all model teams.
* Ensure reliability and quality: Implement CI/CD pipelines for model and data workflows using Docker, Terraform, and Jenkins and share best practices, mentor less experienced engineers, and foster strong collaboration across teams.
* Stay current: Continuously evaluate emerging MLOps technologies to improve efficiency, scalability, and reliability.
Requirements:
* Hands-on MLOps experience: 2+ years production experience operationalizing, deploying, monitoring, and maintaining ML models at scale.
* Tooling: Proficient with infrastructure-as-code, CI/CD systems (Docker, Terraform, Jenkins, or equivalent), and at least one major cloud provider (AWS, GCP, or Azure).
* Programming: Strong Python skills (including ML libraries such as scikit-learn, LightGBM, PyTorch, TensorFlow; plus experience with SQL).
* Monitoring: Familiar with monitoring and logging for ML pipelines.
* Collaboration: Effective communicator with experience partnering across engineering and data science.
* Bonus: Experience with feature stores, model version management, or building internal ML platforms.
What We Offer:
* Work from home flexibility up to 5 days a week.
* International teams.
* Educational budget to support your continuous learning and development.
* Personal growth opportunities.
* Exciting team events like hackathons.
* Discounts on high-quality used cars.