MLOps Engineer - Data Analytics Platform

• developing and managing workflow orchestration using Airflow (core responsibility)
• supporting and improving model lifecycle management using MLflow (tracking, registry, reproducibility)
• actively contributing to the migration of the ML Batch platform from on-premise (IPC) to Google Cloud Platform (GCP)
• refactoring and adapting ML pipelines to run efficiently in cloud-native environments
• developing and improving templates for productionizing ML solutions
• integrating ML pipelines with CI/CD pipelines for automated deployments
• ensuring scalability reliability and reproducibility of ML workloads
• troubleshooting and optimizing pipelines to improve performance and stability
• participating in on-call support to maintain platform reliability
• collaborating with stakeholders to deliver scalable secure and cost-efficient solutions, ML-Batch is a robust, scalable, and efficient platform provided by DAP. The goal of our team is to empower users by providing them with an easy-to-use platform for designing & implementing batch processing, ETL, and machine learning pipelines. By leveraging cutting-edge tools like Airflow & MLFlow and by adhering to MLOps methodology, we aim to facilitate seamless, high-performance data workflows, ensuring that our users can execute their data-driven tasks reliably and efficiently. MLOps practices ensure continuous integration, deployment, and monitoring, enabling a streamlined and collaborative approach to machine learning operations.

As an MLOps Engineer, you will:

• help migrate ML pipelines and workflows to GCP
• contribute to shaping the target ML platform architecture
• work with technologies such as Airflow (orchestration), MLflow (model lifecycle), and Spark (data processing)

You will join a multinational multi-cultural team delivering scalable secure and automated solutions that enable data scientists across ING to build high-impact products.

You will work with modern technologies solve complex problems and help shape the future of ING’s data ecosystem in a cloud-first environment.

The role naming convention in the global ING job architecture will be “Engineer III”

The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations.

• have good understanding of machine learning model deployment and consumption patterns
• have hands-on experience with workflow orchestration tools, especially Apache Airflow (must-have)
• have experience with ML lifecycle management tools such as MLflow (strongly preferred)
• have hands-on experience working with Google Cloud Platform (GCP) in the context of data or ML pipelines (e.g. BigQuery, Vertex AI, Cloud Storage or similar)
• have experience in building containerized components (Docker)
• have experience in CI/CD and DevOps practices
• have hands-on experience with data pipelines and ETL processes
• have hands-on experience with monitoring logging and troubleshooting ML pipelines
• can clearly express ideas and collaborate effectively with data scientists and engineers
• speak English at B2 level or above

You’ll get extra points for:

• strong experience with Airflow-based workflow design and optimization
• experience with Vertex AI in GCP
• experience with Spark or distributed batch data processing
• familiarity with Kedro or similar pipeline frameworks
• experience with Kubernetes or distributed environments