Meghana Nayak
About Meghana Nayak
Meghana Nayak is a Software Engineer with a Master's degree in Computer Science from North Carolina State University. She has experience in developing web service APIs and has worked at various organizations, including Tata Consultancy Services and SparkCognition.
Work at SparkCognition
Meghana Nayak currently serves as a Software Engineer at SparkCognition, a position she has held since 2022. In this role, she focuses on developing software solutions that enhance the capabilities of artificial intelligence and machine learning applications. Her work involves creating efficient and scalable systems that support the company's innovative technology initiatives.
Education and Expertise
Meghana Nayak earned her Master of Science in Computer Science from North Carolina State University, completing her studies from 2020 to 2022. Prior to that, she obtained a Bachelor of Engineering in Computer Science from Jawaharlal Nehru National College of Engineering, where she studied from 2012 to 2016. Her educational background provides her with a strong foundation in software engineering principles and practices.
Background in Software Development
Before joining SparkCognition, Meghana Nayak worked at Tata Consultancy Services as a System Engineer from 2016 to 2019 in Bengaluru, India. During her tenure, she gained experience in software development and project management. She also served as a Graduate Teaching Assistant at North Carolina State University for three months in 2021, where she supported faculty in delivering computer science courses.
Internship Experience at Bloom AI
In 2021, Meghana Nayak completed a one-month internship at Bloom AI in Raleigh, North Carolina. During this internship, she contributed to various projects that focused on artificial intelligence and machine learning technologies. This experience allowed her to apply her academic knowledge in a practical setting and gain insights into industry practices.
Technical Contributions
Meghana Nayak has developed web service APIs that manage the model training lifecycle, including the creation and tracking of hyperparameter optimization jobs. She has implemented REST endpoints to facilitate offline model analysis, enabling model and dataset downloading. Additionally, she contributed to deployment automation by using Helmsman to deploy multiple helm charts in a repeatable manner.