Machine Learning Engineer

Mission & Context

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
• 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