Vishal Doshi
About Vishal Doshi
Vishal Doshi is a Senior Software Engineer with extensive experience in developing AI/ML platforms and data processing tools. He holds a Master of Science in Computer Science from the University of Illinois Chicago and has worked at notable companies including SparkCognition and Uptake.
Work at SparkCognition
Vishal Doshi has been employed at SparkCognition as a Senior Software Engineer since 2018. In this role, he has led the development of an automated end-to-end AI/ML model building MLOps platform. This platform is designed to enable non-data scientists to build and deploy models efficiently. He has also implemented on-demand computation systems for model training, focusing on resilience and cost savings while ensuring data integrity. Additionally, he has utilized Infrastructure as Code (IaC) tools like Helmsman to manage infrastructure effectively.
Education and Expertise
Vishal Doshi holds a Master of Science (M.S.) in Computer Science from the University of Illinois Chicago, where he studied from 2012 to 2015. He also earned a Bachelor of Engineering (B.E.) in Computer Engineering from the University of Mumbai, completing his studies from 2009 to 2012. Prior to that, he obtained a Diploma in Information Technology from the Maharashtra State Board of Technical Education, studying from 2006 to 2009. His educational background supports his expertise in software engineering and data processing.
Background
Before joining SparkCognition, Vishal Doshi worked as a Software Engineer at Uptake from 2016 to 2018. He also held a position as a Software Developer at Egen Solutions during the same period. Earlier in his career, he served as a Graduate Assistant at the University of Illinois at Chicago from 2013 to 2015. His diverse work experience spans various roles in software development and engineering, contributing to his comprehensive skill set.
Technical Skills and Contributions
Vishal Doshi has demonstrated proficiency in several technical areas throughout his career. He has utilized data pipeline orchestration tools like Prefect to enhance data processing efficiency, ensuring robust logging and error tracking. He has also improved and maintained a feature engineering service specifically designed for IIOT time series data. Additionally, he has instrumented metrics collection using Prometheus and Grafana, and developed unit tests and integration tests to uphold code quality.