Machine Learning Engineer

Job Title

Machine Learning Engineer

Location

Brussels, Belgium (Hybrid or Onsite depending on project requirements)

Sector

Insurance / Financial Services

Overview

We are supporting an established organisation within the insurance sector that is expanding its data and machine learning capability. They are seeking a Machine Learning Engineer to design, build, and deploy production-grade ML solutions that support core business functions such as risk modelling, pricing, fraud detection, and customer analytics.

You will work closely with data scientists, data engineers, and business stakeholders to translate analytical models into scalable, maintainable machine learning services.

Key Responsibilities

• Design, develop, and deploy machine learning models into production environments
• Collaborate with data scientists to operationalise models and improve model performance
• Build and maintain end-to-end ML pipelines, including data ingestion, training, validation, and deployment
• Work with structured and unstructured data from internal and external sources
• Ensure models meet requirements around performance, scalability, explainability, and robustness
• Monitor deployed models, manage retraining strategies, and address model drift
• Collaborate with engineering and architecture teams to align with platform and security standards
• Produce clear technical documentation and contribute to best practices around MLOps

Required Skills and Experience

• Proven experience as a Machine Learning Engineer or similar role
• Strong programming skills in Python
• Solid understanding of machine learning algorithms and statistical modelling techniques
• Hands-on experience with ML libraries and frameworks such as scikit-learn, TensorFlow, PyTorch, or similar
• Experience building and deploying ML models in production environments
• Familiarity with MLOps practices, CI/CD, and model lifecycle management
• Experience working with cloud platforms (AWS, Azure, or GCP)
• Strong SQL skills and experience working with large datasets
• Ability to work in a regulated or data-sensitive environment (financial services or insurance experience is a strong plus)

Nice to Have

• Experience with insurance use cases such as pricing, underwriting, claims, fraud detection, or risk modelling
• Exposure to model governance, explainability, or regulatory requirements
• Experience with containerisation and orchestration tools (Docker, Kubernetes)
• Knowledge of data engineering concepts and pipelines

Language

• Working proficiency in English
• French or Dutch is advantageous but not mandatory