Machine Learning Engineer

ING is looking for an experienced Machine Learning Engineer who will be joining an experienced DevOps team within the Global IT Operations tribe of Infrastructure and Engineering in a tech lead role.

The Machine Learning Engineer we are looking for is excited to contribute to the deployment and life cycle management of machine learning driven models for Observability within ING., a key part of our bank’s Observability strategy. Does this sound interesting to you? Please read on.

Global Monitoring Data Pipeline

The Global Monitoring Data Pipeline is the target platform for IT Systems observability signals within ING Globally. We offer standardized collection and processing of metric, log and trace data in OpenTelemetry format for a large number of consumers across the organization. Within this platform, the MDPL-Processing provides integration, enrichment, and transformation capabilities, involving machine learning models based on pattern recognition like anomaly detection and IT Capacity related enhancements like timeseries forecasting. Our analytics execution platform consists of (among others) Spark, OpenShift and Azure DevOps, combined with Python-based models.

Roles and Responsibilities

As a senior machine learning engineer, you will be joining the Area of Global Monitoring. You will be part of a cross functional team that works within a global platform Monitoring Data Pipeline (MDPL). This means working on cross-border solutions and providing services to ING worldwide. You will apply your Software Engineering expertise combined with domain experience to drive product development on Observability related models from a machine learning engineering perspective, engaging with stakeholders on the design of the solutions, shaping the modeling choices from the perspective of compatibility with infrastructure, and shaping platform development based on the intended scope of the use cases to be supported. Your passion is to work with the latest and greatest technologies that make productionizing machine learning models easy, you’re proactive in keeping yourself up to date and experimenting with new technologies. You enjoy laying the architectural foundation while aligning the business problem with stakeholders, while choosing the right technology. You combine both thinking of the future and a hands-on, right now, attitude. You identify on which fronts a team containing data scientists, data engineers and platform engineers can grow and help them get there. You work in a team with highly skilled people and enjoy a creative atmosphere where experimentation is encouraged. You have a learning attitude not only to master new technologies and programming languages, but also on an interpersonal level. You ask and give feedback. You feel at home in a high-performing team and you make the other team members feel at home as well. You have the independence to speak up when you see the need to.

How to succeed 

• Experience with the full machine learning model lifecycle, from design to deployment to running in production, at scale, with proper monitoring on performance;
• Experience in building, operating and/or consuming Machine Learning platforms;
• Experience with building 0-downtime distributed software systems in a highly regulated enterprise environment;

• Experience securely building container images (e.g. Docker), running container workloads in production (e.g. Kubernetes, OpenShift) and orchestrating them (e.g. Airflow);
• Comfortable deploying and running models on Spark at scale in various serving patterns, optimizing execution, and troubleshooting implementations.
• Good understanding of streaming technologies (Kafka or Flink);

• Good understanding of databases; both RDBMS and noSQL (e.g. Cassandra, Redis, InfluxDB, Grafana observability backends);
• Experience with (REST) API design and service meshes;
• Experience with public cloud provisioning technology (e.g. Terraform); 
• Experience with building CI/CD pipelines. Familiarity with Azure DevOps is a plus;

• Experience with working in an agile/scrum way;
• Fluent in written and spoken English (Dutch is not required);

• Experience working with Data Mesh, Data Products, and / or Data Management frameworks is a plus;
• Affinity with analytics use cases in the observability context, or analytics within a site reliability engineering context, is a plus;
• Experience deploying workloads on public cloud environments using cloud native components is a plus;
• Experience with Azure, Azure Fabric, and / or DataBricks is a plus;
• Meaningful contributions to Open Source projects are a plus.

Rewards & Benefits

Working at ING means working in a dynamic and international setting. Individual development of our employees is very important and that is why ING offers excellent courses and programs. We only hire people with exceptional talents and capabilities! You will work on the most innovative projects within ING. In addition, we offer:

• A competitive salary and excellent secondary benefits
• A full time position (36 or 40 hours per week)
• Great training and education opportunities
• Working in an area which is of great importance to the strategy of ING
• Working with highly skilled people
• A relaxed and fun team
• An international atmosphere

About us

With around 60,000 employees and operations in approximately 40 countries, there is no shortage of opportunities for people with initiative who want to make a difference. We hire smart people like you for your potential, not your past. Our biggest expectation is that you’ll stay curious. Keep learning. Take on more responsibility. In return, we’ll back you to develop into an even more awesome version of yourself. If you want to work at the cutting edge of what’s possible, surrounded by progressive, inspiring and supportive colleagues, there is no better place to invest your talents than at ING. Join us!