Medior Machine Learning Engineer, RBA @ ING Netherlands

The Team 

Within ING, there is ING Analytics (INGA) - a major driving force in its digital transformation of the bank. 

By creating measurable value for ING and its customers through world-class analytics products and services, INGA helps ING to become a leader in data-driven decision-making. Among others, INGA delivers call & speech analytics, generative AI in risk summarization, insights into portfolio performance, advanced ESG data insights (commercial purposes), data analytics platforms (cloud and on-premises), and many more. 

Roles and Responsibilities 

We are looking for a MLE to join us in Amsterdam. In this role, together with the data scientists, you will be responsible for streamlining the process of taking machine learning models to production, maintaining, and monitoring them. You will participate in designing and building the models, data pre-processing, performance tuning of ML models, and more. 

We are looking for curious and empathetic individuals who are committed to developing themselves and learning new skills. We hire people based on an evaluation of their potential and support them throughout their time in Amsterdam. Come join us! 

A working day in the life of a Machine Learning Engineer 

A typical day for a machine learning engineer starts with coffee, catching up with colleagues, and drafting a plan for the coming day. After the standup, you may start analyzing algorithms to identify which of them works well with the problem you are trying to solve. For the rest of the morning, you could experiment in Jupyter notebook using Scikit Learn or use IDE to continue developing a class that implements a model or interfaces with the database. 

After lunch, meet with the team to discuss the next iteration of the product, or go over new features that need to be implemented and discuss how to create or calculate those features. Wrap up the day working on the monitoring tool for machine learning models. 

How to succeed 

Minimum Qualifications 

• Bachelor's degree or equivalent practical experience. 

• Excellent communication skills, capable of working collaboratively in a semi-remote, globally distributed team.  

• Hands-on experience building complex data pipelines (e.g., in Apache Spark). 

• Hands-on experience with technologies and frameworks used in ML (e.g., sklearn, MLFlow, etc.).

• Knowledge of software engineering best practices (versioning, testing, documentation, etc.).

Preferred Qualifications 

• Curious mindset; you are collaborative and passionate about helping others to grow and improve. 

• Knowledge of MLOps architecture and practices. 

• Experience with monitoring and observability. 

• Experience with deployment and provisioning automation tools and concepts, e.g., Docker, Openshift, CI/CD. 

• Good understanding of different data storages (e.g., RDBMS, non-SQL, and time-series databases).