Panos Vardanis

Panos Vardanis

Senior Ml Ops Engineer @ Wallaroo

About Panos Vardanis

Panos Vardanis is a Senior MLOps Engineer at Wallaroo.AI in Berlin, Germany, where he has worked since 2023. He has extensive experience in machine learning and data science, having held various roles in companies such as Utopia Music and Loudly.

Work at Wallaroo

Panos Vardanis has been employed at Wallaroo.AI as a Senior MLOps Engineer since 2023. In this role, he focuses on optimizing machine learning operations and enhancing workflow efficiency. His position is based in Berlin, Germany, where he contributes to the company's mission of streamlining machine learning processes.

Previous Experience in Machine Learning

Prior to his current role, Vardanis held various positions in the machine learning field. He worked at Utopia Music as a Senior MLOps Engineer from 2022 to 2023 and as a Machine Learning Engineer from 2021 to 2022. He also served as a Data Scientist at Loudly from 2020 to 2021. His experience spans multiple aspects of machine learning, including engineering and operations.

Education and Expertise

Panos Vardanis earned an Integrated Masters in Electrical and Computer Engineering, specializing in Telecommunications and Information Technology, from the University of Patras. His educational background provides a strong foundation for his work in machine learning and data science, contributing to his comprehensive understanding of the machine learning lifecycle.

Background in Research and Development

Vardanis has experience in research and development, having worked as a Research Associate at the University of Sussex from 2019 to 2020. He also worked as an Audio DSP Engineer at SoundTalks in 2019. These roles have contributed to his technical skills and understanding of advanced machine learning applications.

Advocacy for Clean Code Practices

Panos Vardanis advocates for clean code and SOLID principles in software development. He emphasizes the importance of creating modular and maintainable production-ready code, which is essential for efficient machine learning workflows. His commitment to these practices enhances the quality and reliability of the projects he works on.

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