Devendra Parhate
About Devendra Parhate
Devendra Parhate is a Technical Lead currently working at Acceldata in Bengaluru, India. He has extensive experience in machine learning and data analytics, having held positions at companies such as Jupiter, Elocity, and UnitedHealth Group.
Current Role at Acceldata
Devendra Parhate serves as a Technical Lead at Acceldata, a position he has held since 2023. His role involves overseeing technical projects and leading teams to enhance data infrastructure solutions. He works on-site in Bengaluru, Karnataka, India, contributing his expertise in machine learning and data analytics.
Previous Experience at Jupiter
Prior to his current role, Devendra worked at Jupiter as a Senior Machine Learning Engineer from 2021 to 2023. During his two years at the company, he focused on developing machine learning models and algorithms, which included implementing recommendation systems and optimizing data processes. His work was conducted on-site in Bengaluru, Karnataka, India.
Experience at Elocity
Devendra held two positions at Elocity. Initially, he worked as a Lead Software Engineer for seven months in 2020, followed by a role as Platform Architect for five months in 2021. In both roles, he contributed to the development of software solutions and infrastructure, enhancing the company's data capabilities in Bengaluru, Karnataka, India.
Background in Software Engineering
Devendra's career includes significant experience at UnitedHealth Group, where he served as Software Engineer II - OptumInsight from 2017 to 2020. His responsibilities included building distributed system architectures and working with micro-services. He also completed an internship at Vellore Institute of Technology in 2017.
Education and Expertise
Devendra Parhate earned a Bachelor's degree in Computer Science from Vellore Institute of Technology. His educational background laid the foundation for his proficiency in programming languages such as Java, Scala, Rust, and Python, particularly in the context of data analytics and machine learning at Exabyte scale.