The Machine Learning Engineering team is part of the central Data Platform department at idealo. It has the mission to enable the rest of the company to build ML-based products. Our approach is a combination of internal consulting and development, along with building platform products that speed up the development of new ML workflows.
We are looking for a Machine Learning Engineer with a focus on Machine Learning Operations (MLOps), who complements the established team of machine learning engineers especially in building infrastructure that supports the entire ML lifecycle management. A successful candidate would be highly passionate about streamlining and automating processes that are required to develop, build, and operate ML workloads at scale and be aware of the additional complexities that MLOps brings compared to DevOps.
About your new role
- Identify and build ML platform products primarily on AWS
- Establish best practices around MLOps that foster fast iteration cycles and high-quality standards for ML solutions in production
- Work with data science teams across the company and help them build high quality, scalable ML solutions
- Be an active part of our ML community and proactively communicate with other team members and project stakeholders
Skills & Requirements
- You are experienced in building solutions on AWS through infrastructure as code (Terraform, AWS CloudFormation)
- You have worked with CI/CD tools like Jenkins, Argo, or AWS CodePipeline
- Ideally you are experienced in ML production workflows and the associated technologies (Kubernetes, Docker)
- Ideally, you are experienced in setting up and running ML platforms like MLflow or AWS SageMaker
- You can prove strong hands-on experience with programming in Python
- You understand the workflow of machine learning and how DevOps principles can be applied to scale machine learning solutions
- You have a solid understanding of good practices in software engineering, like pair programming, testing, clean code
- You communicate clearly, transparently, and you are fluent in English