Machine Learning Engineer
Role details
Job location
Tech stack
Job description
Imagine working at the intersection of business and engineering, where your work directly contributes to protecting customers and safeguarding financial systems. As a Machine Learning Engineer, you will play a pivotal role in bridging business needs and IT development, focusing on high-impact areas such as Financial Economic Crime (FEC) and Customer Due Diligence (CDD). You will help design, build, and operate scalable machine learning platforms and pipelines, with a strong focus on automation, standardization, and regulatory-compliant model delivery. Your work will directly support secure, efficient, and reliable deployment of machine learning models in a complex enterprise environment., * Work on a critical banking domain: Financial Economic Crime (FEC).
- Develop, manage, and maintain Azure infrastructure and Databricks workspaces tailored for high-performance ML and AI use cases
- Build and maintain scalable machine learning pipelines across the full ML lifecycle in close collaboration with data scientists and engineering teams
- Ensure the health and reliability of ML systems by monitoring data quality, model performance, and business impact, and defining mitigation strategies where needed
- Identify bottlenecks and improvement opportunities in the ML development cycle and introduce tools to improve development, testing, and deployment efficiency
- Implement solutions in line with architectural guidelines and engineering best practices
- Continuously evolve your technical expertise by staying up to date with the latest ML, data, and cloud technologies
Requirements
- Eager to learn, solution-oriented, and collaborative by nature
- Completed HBO or WO degree in a relevant field (e.g., Computer Science, Data, Engineering)
- Minimum 4 years of relevant experience in Software, Data, or ML Engineering, including at least 2 years in Machine Learning Engineering
- Strong hands-on experience with Python and PySpark, ideally working with large datasets
- Experience with Azure Databricks is a strong plus
- Solid software engineering practices, including Git, release management, unit testing, and testing strategies
- Experience working in a cloud environment, preferably Microsoft Azure
- Familiar with setting up and managing CI/CD pipelines
- Experience working in Agile and/or Scrum development environments
- Passionate about building scalable, efficient, and robust data and ML solutions
Benefits & conditions
- A competitive salary based on your qualities and experience
- NS business card to cover your commute expenses
- 25 days of paid holiday per year
- A laptop and a smartphone
- A pension scheme
- Health insurance
- Organization driven by technology - we have a tremendous technology backbone
- Access to Udemy, Cognizant Academy digital libraries for your continuous learning
- Open, 'can do' team spirit and international environment that encourages making your ideas reality!