Graduate Software Development Engineer

• Jul 2023 - Present

Software Developer Intern

• Jan 2023 - Jun 2023

Externship

• Sep 2022 - Nov 2022

● Snapshots and Backup Copy- Joined as a part of the Intellisnap team. ● Automation Tasks- Developed and remotely executed 20+ shell and Python scripts on virtual machines (Unix and Windows). ● Snapshots and Backup Copy- Joined as a part of the Intellisnap team. ● Automation Tasks- Developed and remotely executed 20+ shell and Python scripts on virtual machines (Unix and Windows).

Pratidhi mentee

• Mar 2022 - May 2022

● Selected among the top 40 out of 1500. Gaining practical software engineering experience from Commvault experts. ● Developing strong problem-solving sense, re-learning concepts from an industry point of view, can implement in actual projects ● Selected among the top 40 out of 1500. Gaining practical software engineering experience from Commvault experts. ● Developing strong problem-solving sense, re-learning concepts from an industry point of view, can implement in actual projects

Machine Learning Intern

• Dec 2020 - Apr 2021

Facial Features Recognition (Object Detection) ● Webscrapped more than 1000 images from Pinterest. Annotated the images using coco annotator. ● Boosted existing model accuracy from 67% to 81%. Trained custom ConvNets with residual connections from scratch. ● Superimposed 1 or more images (sunglasses, hats, etc.) over the coordinates obtained from the model using OpenCV and Pillow. Human Skin Recognition (Image Segmentation) ● Collected Image dataset from pixabay.com. Utilized 3rd… Show more Facial Features Recognition (Object Detection) ● Webscrapped more than 1000 images from Pinterest. Annotated the images using coco annotator. ● Boosted existing model accuracy from 67% to 81%. Trained custom ConvNets with residual connections from scratch. ● Superimposed 1 or more images (sunglasses, hats, etc.) over the coordinates obtained from the model using OpenCV and Pillow. Human Skin Recognition (Image Segmentation) ● Collected Image dataset from pixabay.com. Utilized 3rd party data annotation tools. Used skin color augmentation. ● Used U-Net ConvNet (of accuracy upto 83%) to generate skin segmentation mappings that could specifically distinguish human skin from the backdrop. Show less Facial Features Recognition (Object Detection) ● Webscrapped more than 1000 images from Pinterest. Annotated the images using coco annotator. ● Boosted existing model accuracy from 67% to 81%. Trained custom ConvNets with residual connections from scratch. ● Superimposed 1 or more images (sunglasses, hats, etc.) over the coordinates obtained from the model using OpenCV and Pillow. Human Skin Recognition (Image Segmentation) ● Collected Image dataset from pixabay.com. Utilized 3rd… Show more Facial Features Recognition (Object Detection) ● Webscrapped more than 1000 images from Pinterest. Annotated the images using coco annotator. ● Boosted existing model accuracy from 67% to 81%. Trained custom ConvNets with residual connections from scratch. ● Superimposed 1 or more images (sunglasses, hats, etc.) over the coordinates obtained from the model using OpenCV and Pillow. Human Skin Recognition (Image Segmentation) ● Collected Image dataset from pixabay.com. Utilized 3rd party data annotation tools. Used skin color augmentation. ● Used U-Net ConvNet (of accuracy upto 83%) to generate skin segmentation mappings that could specifically distinguish human skin from the backdrop. Show less