Financiële diensten
Data Engineer / MLOps Engineer for production data & AI solutions (regulated environment)
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Role overview
This position supports the engineering and operations of data and (AI) solutions used in sanctions controls within a regulated financial-crime technology environment. The role is part of a sanctions technology innovation team that builds, improves, and maintains data-driven solutions in collaboration with domain experts, risk stakeholders, and investigators. The team delivers production-ready capabilities by combining data engineering and operational disciplines with data science and analytics.
Key responsibilities
Design, build, and deploy production-grade data and AI solutions. Own the data engineering and platform components that allow models and analytical solutions to be ingested, transformed, orchestrated, and deployed in a secure, compliant, and robust manner. Provide technical input to solution design, work closely with data scientists and IT engineers, and ensure operational reliability in a production-critical setting. Contribute to continuous improvement by identifying issues related to data quality, performance, or architecture and following through on remediation. Communicate clearly across technical and non-technical stakeholders and share knowledge to strengthen team ways of working.
Current assignments
Engineering work will focus on reducing false-positive alerts through improved automated handling, supporting investigators for more complex alerts with AI-generated insights, contributing to monitoring controls aimed at detecting attempts to circumvent sanctions restrictions, and supporting the migration of sanctions controls for trade-related processes to a new platform.
Eisen
- 5+ years of experience in DevOps, MLOps or Data Engineering
- Experience deploying statistical models into production while adhering to strict policies and governance frameworks
- Ability to define coding standards for production environments
- Experience with a major cloud provider and CI/CD practices, including designing and maintaining pipelines for automated testing, deployment, and integration
- Strong proficiency in Python and PySpark, complemented by experience with workflow orchestration and model management tools such as Apache Airflow and MLflow