Machine Learning Engineer

il y a 4 semaines


Brussels, Belgique Montash Temps plein

Job TitleMachine Learning EngineerLocationBrussels, Belgium (Hybrid or Onsite depending on project requirements)SectorInsurance / Financial ServicesOverviewWe 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 ResponsibilitiesDesign, develop, and deploy machine learning models into production environmentsCollaborate with data scientists to operationalise models and improve model performanceBuild and maintain end-to-end ML pipelines, including data ingestion, training, validation, and deploymentWork with structured and unstructured data from internal and external sourcesEnsure models meet requirements around performance, scalability, explainability, and robustnessMonitor deployed models, manage retraining strategies, and address model driftCollaborate with engineering and architecture teams to align with platform and security standardsProduce clear technical documentation and contribute to best practices around MLOpsRequired Skills and ExperienceProven experience as a Machine Learning Engineer or similar roleStrong programming skills in PythonSolid understanding of machine learning algorithms and statistical modelling techniquesHands-on experience with ML libraries and frameworks such as scikit-learn, TensorFlow, PyTorch, or similarExperience building and deploying ML models in production environmentsFamiliarity with MLOps practices, CI/CD, and model lifecycle managementExperience working with cloud platforms (AWS, Azure, or GCP)Strong SQL skills and experience working with large datasetsAbility to work in a regulated or data-sensitive environment (financial services or insurance experience is a strong plus)Nice to HaveExperience with insurance use cases such as pricing, underwriting, claims, fraud detection, or risk modellingExposure to model governance, explainability, or regulatory requirementsExperience with containerisation and orchestration tools (Docker, Kubernetes)Knowledge of data engineering concepts and pipelinesLanguageWorking proficiency in EnglishFrench or Dutch is advantageous but not mandatory


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