Summary
Join Xebia, a global leader in digital solutions, and contribute to the development and deployment of machine learning systems for e-commerce websites, in-store portals, and mobile apps. As a key member of our team, you will establish efficient data processes, write scalable software, and promote best practices in software engineering. This role requires extensive experience in machine learning, data engineering, and cloud technologies (AWS, Azure, GCP). You will collaborate with data scientists and analysts to deliver innovative product features. We offer a dynamic work environment focused on continuous learning and development. Apply now to begin your journey with Xebia!
Requirements
- Ability to start immediately
- 3+ years of experience developing and deploying machine learning systems into production
- 5+ years of experience as a data engineer or software developer
- Knowledge of MLOps architecture and practices
- Different programming skills like Python, Go and/or JAVA, as well as programming in a statically typed language e.g. Java, Scala, Go
- Expertise in public cloud (AWS, Azure or GCP)
- Experience in managed GCP services (GKE, GCS, BQ, Dataproc, Dataflow)
- Knowledge of public cloud analytics
- Expertise in managed Azure services, as well as managed AWS services
- Relevant work experience in: - sklearn - MLFLow β TensorFLow
- Experience in Kubernetes
- Monitoring, observability, logging, alerting
- Expertise in Apache Airflow and MLflow
- Very good verbal and written communication skills in English
- Work from the European Union region and a work permit are required
Responsibilities
- Work with data scientists and analysts to create and deploy new product features on the e-commerce website, in-store portals, and clientsβ mobile apps
- Establish scalable, efficient, automated processes for data analysis, model development, validation, and implementation
- Write efficient and scalable software to ship products in an iterative, continual-release environment
- Contribute to and promote good software engineering practices across the team and building cloud-native software for ML pipelines
- Contribute to and reuse community best practices