Sergej Tretjakov
About Sergej Tretjakov
Sergej Tretjakov is a Staff Software Engineer at PandaDoc in Lisbon, Portugal, with a diverse background in software engineering and management. He has held various roles at WorkFusion, including Director of Engineering and Solutions Architect, and has a strong educational foundation in system analysis and information processing.
Work at PandaDoc
Sergej Tretjakov has been employed at PandaDoc as a Staff Software Engineer since 2022. He is based in Lisbon, Portugal, and has contributed to various engineering projects during his tenure. His role involves overseeing software development processes and ensuring the implementation of best practices in engineering.
Previous Experience in Engineering
Prior to his current position, Sergej Tretjakov held several roles at WorkFusion. He served as Director of Engineering for one year in 2018, Team Lead for two years from 2016 to 2018, Senior Java Engineer from 2014 to 2016, and Solutions Architect from 2021 to 2022. He also worked as Head of Delivery Engineering, EMEA, for 11 months in 2020. His earlier experience includes a role as a Junior Java Developer at SaM Solutions from 2011 to 2012 and as a Java Engineer at GP Solutions from 2012 to 2013.
Education and Expertise
Sergej Tretjakov earned a Master's degree in System Analysis, Management, and Information Processing from Belarusian State University of Informatics and Radioelectronics, studying from 2013 to 2015. He also holds a Bachelor's degree in Automated Systems of Information Processing from the same university, which he completed from 2008 to 2013. His educational background provides a strong foundation in software engineering and systems management.
Technical Contributions and Projects
During his career, Sergej Tretjakov has made significant contributions to machine learning and cloud computing. He improved the ML training pipeline for Sagemaker, enhancing its efficiency by approximately two times. Additionally, he conducted performance tests for ML models and end-to-end tests for business flow integration. He adopted CI/CD practices for ML models to support a multi-regions setup in AWS, focusing on optimizing costs and addressing security issues within the AWS account.