Ankesh Pandey
About Ankesh Pandey
Ankesh Pandey is a Machine Learning Engineer 4 at Adobe, where he has worked since 2020. He holds a B.Tech in Electrical Engineering from the Indian Institute of Technology, Kanpur, and an M.Tech in Software Systems with a specialization in Data Analytics from BITS Pilani.
Work at Adobe
Ankesh Pandey has been employed at Adobe since 2019, holding multiple roles including Algorithm Engineer and currently as Machine Learning Engineer 4. His tenure at Adobe spans over four years, during which he has contributed to various deep learning projects. His responsibilities include making strategic decisions and executing key initiatives in machine learning, showcasing his leadership and planning capabilities in technical environments.
Education and Expertise
Ankesh Pandey earned a Bachelor of Technology (B.Tech) in Electrical Engineering from the Indian Institute of Technology, Kanpur, from 2012 to 2016. He further pursued a Master of Technology (M.Tech) in Software Systems with a specialization in Data Analytics at BITS Pilani from 2020 to 2022. He also completed a Post Graduate Program in Artificial Intelligence and Machine Learning at Great Learning in 2020. His academic background equips him with a strong foundation in statistical learning theory and proficiency in tools such as TensorFlow and PyTorch.
Background
Before joining Adobe, Ankesh Pandey worked at VMock as a Senior Machine Learning Engineer from 2017 to 2019, where he advanced from a Machine Learning Engineer role. His career began with a Data Science Internship at Wipro Infotech in 2015. His experience spans various aspects of machine learning and data science, providing him with a comprehensive understanding of the field.
Achievements
Ankesh Pandey has demonstrated expertise in deep learning and statistical learning theory, which are essential for developing effective machine learning algorithms. His ability to make strategic decisions in technical projects reflects his leadership skills. His work at Adobe and previous roles at VMock highlight his contributions to the field of machine learning.