Krutika Bapat
Software Engineer 2 at Microsoft
Hyderabad, Telangana, India
Summary
👤
Senior🎓
Top SchoolKrutika Bapat is a software engineer with 9 years of experience focusing on deep learning, machine learning, and computer vision, currently contributing to Microsoft’s Azure SQL DB Cloud + AI team. A B.Tech in CSE from IIIT-NR, she has blended research and industry exposure through internships at IIT Bombay, NTU, American Express AI Labs, and Big Vision LLC, and now works at Microsoft as Software Engineer 2. She has practical expertise in Python, C/C++, SQL, OpenCV and Keras, and has contributed to the widely used learnopencv repository by building synthetic-data pipelines and CNN-based character classification models. Comfortable moving models from research to production, she has a track record of designing synthetic image generation, augmentation, and inference scripts that tackle real-world distortions. Active in academic and community leadership during college, she brings both technical depth and a penchant for applied research to cloud-scale AI challenges.
9 years of coding experience
3 years of employment as a software developer
High School, High School at Kendriya Vidyalaya RaipurBachelor of Technology Computer Science, Bachelor of Technology Computer Science at International Institute of Information Technology Naya Raipur
English, Hindi, Marathi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (12)
mask-rcnn10
keras10
computer-vision10
faster-rcnn10
opencv10
machine-learning10
deep-learning10
python10
image-processing10
numpy9
tensorflow9
ai8
Programming languages (5)
Github contributions (5)
spmallick/learnopencv
Jun 2018 - Apr 2019
Learn OpenCV : C++ and Python Examples
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 9 PRs in 9 months
Contributions summary:Krutika primarily contributed to character classification for a synthetic dataset within the context of computer vision and deep learning. They developed Python scripts using libraries like `numpy`, `opencv`, and `keras` for image generation and model training. These scripts involved generating synthetic images with perspective distortions, blur, and noise, followed by the implementation of a convolutional neural network (CNN) for character recognition, and the implementation of a script to make predictions with the trained model.
opencv-librarypythondeep-learningopencv3computer-vision
krutikabapat/krutikabapat.github.io
Jun 2019 - Dec 2019
Build a Jekyll blog in minutes, without touching the command line.
Contributions:192 pushes in 6 months
jekyll-blogjekylljekyll-thememinutes
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