Machine Learning Engineer (Senior)

As an ML Engineer in StoreOps, you will dive into projects that streamlining retail operations through the use of analytics and ML, by applying your Python, Spark, Kubernetes, and Cloud (Azure) skills. You will be contributing to a mix of mature and new projects by bringing machine learning pipelines into production, building and maintaining robust Azure infrastructure, as well as fostering a technical culture of the organization.

- Developing machine learning models and feature engineering pipelines with cooperation with data scientists.
- Working in an Azure cloud environment, developing model training code in AzureML.
- Building and maintaining cloud infrastructure with IaC (infrastructure as code).
- Working with distributed data processing tools such as Spark, to parallelise computation for Machine Learning.
- Diagnosing and resolving technical issues, ensuring availability of high-quality solutions that can be adapted and reused.
- Collaborating closely with different engineering and data science teams, providing advice and technical guidance to streamline daily work.
- Championing best practices in code quality, security, and scalability by leading by example.
Taking your own, informed decisions moving a business forward.

Python, PySpark, Airflow, Docker, Kubernetes, Azure (incl. Azure ML), KServe, Feathr, Dask, xgboost, pandas, scikit-learn, numpy, GitHub Actions, Azure DevOps, Terraform, Git @ GitHub

- Serving machine learning models online based on an online feature store.
- Enhancing the monitoring, reliability, and stability of deployed solutions, including the development of automated testing suites.
- Automating the machine learning model lifecycle to continuously improve the performance on production.
- Collaborating with cross-functional teams enhancing customer experiences through innovative technologies.

5 engineers