I have a background centered around manufacturing, alternating between applied engineering research and scientific roles. Starting in robotics and automation, I transitioned to a data science masters with focus on computer vision and MLOps. Throughout my career, I’ve rotated through different R&D departments, which exposed me to a variety of projects ranging from academic to technical challenges including predictive maintenance, fine-grain image analysis, multi-task learning. Having recently transitioned to a role as an ML Engineer, I am now focusing on establishing robust MLOps practices to guide projects from concept to production maturity. My goal is to build infrastructure that streamlines the deployment process and enables us to transform successful machine learning PoC-s into tangible business impact.

During my studies and worklife, I have been enjoying great guidance and insights from professors and industry mentors, which shaped my career direction significantly and let me meet a lot of great people. I think it is very relevant to create these relationship for growing one’s network, getting exposed to new topics or perspectives and to be able to pose questions to someone outside one’s peer group. I am still relatively early in my career with only 4 years of industry experience, which means that the experience of transitioning from studies to worklife is still fresh in my memory and I would be happy to pay forward all the guidance I got along the way!