ML Engineer

Job Description Machine Learning Engineer - Senior Mission Context Machine Learning Engineers promote best practices in industrial code development across the ML & AI community. They develop production-ready ML pipelines and integrate existing ML solutions into industrial environments. They contribute to AI service development, deployment, monitoring, data quality checks, data flow design, model design, and production integration. They also facilitate communication between AI & Analytics teams and IT Production to ensure properly deployed and monitored ML models. Function Description Work with Data Scientists to define and develop target solutions while considering production constraints. Select appropriate infrastructure and serving models based on business requirements and processing needs. Automate ML pipeline components for production deployment. Build Docker or VM images and prepare unit, regression, and integration tests. Support Data Scientists in using industrial AI service and CI/CD solutions. Support IT Production with environment parameterization. Ensure models run without errors, are retrained when required, and are monitored from both IT and business perspectives. Language Requirements Dutch: Plus French: Plus English: Mandatory Telework 50% onsite and 50% homeworking Job Description - Grade Specific Required Experience At least 4 years of relevant experience Technical Experience (Mandatory) Containerization and virtualization AI platforms and IDEs CI/CD (GitLab CI) Code, model and data versioning Advanced Python Package and dependency management PostgreSQL Technical Experience (Preferred) Integration technologies including distributed and mainframe environments Model compression techniques ELT/ETL tools Big Data tools such as Spark Data flow processing Data visualization tools Business Experience (Mandatory) Knowledge of Agile methodology Soft Skills Strong written and verbal communication Results driven Attention to detail Creativity and problem solving Continuous learning mindset Efficiency and effectiveness awareness Out-of-the-box thinking Ownership and accountability Open to change, feedback and collaboration