Saurabh Powar

AI Enthusiast & Passionate Full Stack Developer 🚀

I am a final year undergraduate student at Veermata Jijabai Technological Institute, majoring in Electronics Engineering.

Currently I am working as a Software Developer Intern at GroundUp, India.

My Interests are:

  • Machine Learning
  • Deep Learning
  • Software Development
  • Cross-platform Application Development
  • Open Source

If you are inquisitive about DL you can read my blogs at hashnode.com/@sp-lit5

Experience

Milvus @ OSPP Summer 2022

Open Source Developer
June 2022 - Present
  • Developing a Face Recognition Pipeline using Milvus Database, Image Processing Algorithms and Deep learning.
  • The procedure includes Face detection using MTCNN, storing the data of Face embeddings on Milvus Database and Face classification.

GroundUp, Inc.

Software Developer Intern
Feb 2021 - Present
  • Actively developing new features inside the React Native based GroundUp Mobile Applications, and the Admin Panel web application for monitoring the entire ecosystem.
  • Fixed issues and bugs in the present builds, optimized workflow of the applications.
  • Written Unit tests for testing different components inside the application using React native oriented frameworks like Jest and Enzyme.

iNeuron.ai, Inc.

AI Intern
Dec 2021 - Jan - 2022
  • Developed a solution to a problem statement Automated Invoice Processing with OCR and Deep Learning.
  • Worked on Digitizing an Invoice which includes extracting pertinent information/data from scanned or PDF invoices and transforming it into a machine readable format that is both editable and searchable.

ROS @ OSPP Summer 2021

Open Source Developer
July 2021 - Sept 2021
  • The Institute of Software Chinese Academy of Sciences and OpenEuler selected me for the coveted Open Source Promotion Plan initiative.
  • Developed safety watchdog node in C++ to ensure the mobile robot is acting properly and not about to collide with an obstacle based on lidar, RGBD data.

AIRPIX, Inc.

AI, ML, and Edge Computing Intern
May 2021 - Aug 2021
  • Under the problem statement, lead the work of detecting lane markers and tracking their consistency using Computer vision and deep learning techniques.
  • Trained YOLOv5 models for Road assets objects detection, achieved a reasonable mAP of around 87%.