Summary
Aleksei Maliutin is a Machine Learning and MLOps engineer with 11 years of experience building production-grade ML systems and shipping them at scale from research notebooks to Kubeflow/KServe deployments. Based in Amsterdam, he combines strong modeling chops (TensorFlow, XGBoost, attention models) with hands-on MLOps delivery—leading a team that built a security-enhanced Kubeflow platform on Azure AKS now used across his department. His work spans NLP, anomaly detection, and graph-based financial representation learning, and his University of Amsterdam thesis introduced a novel finWalk sampling strategy for financial networks. He measures success by operational impact, having cut procedure times and training times significantly through pipeline and serving optimizations. A double MSc graduate and Microsoft-certified practitioner, he mentors junior engineers and actively shapes MLOps roadmaps within enterprise settings.
11 years of coding experience
3 years of employment as a software developer
BSc Computer Science Informatics and Information Technologies , BSc Computer Science Informatics and Information Technologies at Saint Petersburg State University
Master of Science - MS Computational Science (joint degree), Master of Science - MS Computational Science (joint degree) at Vrije Universiteit Amsterdam (VU Amsterdam)
Master of Science - MS Computational Science (joint degree), Master of Science - MS Computational Science (joint degree) at University of Amsterdam
MSc Computer Science BigData and Extreme Computing, MSc Computer Science BigData and Extreme Computing at ITMO University
Russian, English