Mission & Context
Increase your chances of reaching the interview stage by reading the complete job description and applying promptly. The Machine Learning Engineer plays a key role in enabling the industrialization of Machine Learning and AI solutions within the enterprise. The mission of the role is to promote and apply best practices in production-ready ML development, ensuring that AI solutions are robust, scalable, monitored, and fully integrated into IT production environments. Machine Learning Engineers bridge the gap between AI & Analytics teams and IT production, ensuring that Machine Learning models deployed to production are supported by appropriate data pipelines, infrastructure, automation, and monitoring from both a technical and business perspective. They contribute to the full lifecycle of AI services, from design and development to deployment, monitoring, and continuous improvement. Required Experience & Knowledge Minimum 4 years of relevant experience as a Machine Learning Engineer, ML Platform Engineer, or similar role Technical Skills Strong experience with containerization and virtualization (Docker, VMs) Experience with AI platforms and development environments CI/CD pipelines, preferably GitLab CI Code, data, and model versioning practices Advanced Python development xjsrcvq Package management and dependency management PostgreSQL Preferred Experience integrating systems across different technologies (distributed systems, mainframe environments) Model optimization and compression techniques ELT / ETL tools Big data technologies (e.g. Apache Spark) Data flow processing frameworks Data visualization tools