Experience

AppZen

• United States
• Software Development
• 200 - 300 Employee

• Senior Al Software Engineer

  • Apr 2019 - Present

  Building End to End ML/DL models and platforms Skill Set and Experience: Natural Language Processing, Computer Vision, PyTorch, TensorFlow, Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Supervised/Unsupervised ML classifiers, Knowledge Graph Kubernetes CI/CD, Argocd, Helm charts, Seldon, Dockers, CI/CD pipelines with Jenkins, Google Protobufs, Sqlalchemy, Creating APIs with Flask Apps, Elasticsearch, Kafka, Appache Flink, Airflow Building End to End ML/DL models and platforms Skill Set and Experience: Natural Language Processing, Computer Vision, PyTorch, TensorFlow, Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Supervised/Unsupervised ML classifiers, Knowledge Graph Kubernetes CI/CD, Argocd, Helm charts, Seldon, Dockers, CI/CD pipelines with Jenkins, Google Protobufs, Sqlalchemy, Creating APIs with Flask Apps, Elasticsearch, Kafka, Appache Flink, Airflow



Tesla

• United States
• Motor Vehicle Manufacturing
• 700 & Above Employee

• Senior Product Engineer - Analytics and Optimization

  • May 2016 - Mar 2019

  Development of Analytical methods for data analysis and design optimization on a regular basis: • Developed Regression models + trained them using field and physical test data to minimize the cost through stochastic gradient descent. • Developed and trained Classification Models using Labeled field data • Developed pipelines to automate data pre/post-processing as well as fit the models to data • Developed scripts on top of design validation software to perform statistical analysis on the initial design and guide test engineers to the right locations in the field to measure data. • Continuously trained models and worked on the correlation between experiments and simulation data to ensure the continous improvement of precision and recall of developed models. Show less



Ford Motor Company

• United States
• Motor Vehicle Manufacturing
• 700 & Above Employee

• Product Engineer - Analytics and Optimization

  • Mar 2013 - Apr 2016

  Development of Analytical methods for data analysis and design optimization on a regular basis: • Developed Regression models + trained them using field and physical test data to minimize the cost through stochastic gradient descent. • Developed Classification Models using Labeled field data and continuously trained them • Developed pipelines to automate data pre/post-processing as well as fit the models to data (language: Python/C++/TCL/TK) • Developed scripts on top of design validation software to perform statistical analysis on the initial design and guide test engineers to the right locations in the field to measure data. • Continuously trained models and worked on the correlation between experiments and simulation data to ensure the continous improvement of precision and recall of developed models. Show less



University of Michigan

• United States
• Higher Education
• 700 & Above Employee

• Postdoc Research fellow

  • 2012 - Feb 2013

  Atomistic Computer Modeling of Li-ion Battery Atomistic Computer Modeling of Li-ion Battery