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Immediate joiner || Lead AI Engineer at AIRA Matrix || GenAI || Computer Vision || MLOps || BITS Pilani
"Hello World"- We have adopted this phrase as a sign to greet, sustenance, creativity and a sign of life. This serves as an apt expression to breathe life into the following sentences as I begin to describe my journey as a Data Scientist. Through a mixture of Coursera and self-learning, it was during my bachelor's that I recognized that the ability to process and interpret data in an efficient manner presented us to make the world a better place by providing us with the power to make data-driven decisions. Following some setbacks in my journey early on in my quest, I kept on laboriously researching the latest trends and solutions to stay abreast, recognizing overnight success stories are few and far between. Somehow, through a combination of self-interest and acquainting myself with great minds, I came across the fascinating world of Computer Vision which enables machines to replicate the human visual system, signalling a feeling of "Eureka!" in me- a reason for my being and a never-ending passion to yearn to learn, exploring and tackling problems in this domain. I have extensively worked on implementing cutting-edge technology directly from research papers, and strive to be a continuous practitioner of learning by doing philosophy, thus aiming to provide value to any project I am tasked (or task myself) upon. It also entails that in my spare time, I love to read research papers and endeavour to code simplified implementations of them using the Tensorflow framework for deep learning. Having said so, I have been quite the fortunate one, having had a management structure and teams around me to assist me, with the best exhibit being my current set-up here at Eagleview. I am responsible for developing applications centred on computer vision to refine and create solutions for multi-scale geospatial object recognition and segmentation in high spatial resolution remote se
Birla Institute of Technology and Science, Pilani
Master's degree, Computer Science, specialization - Machine Learning and Artificial Intelligence
April 1, 2023 – April 1, 2025
Chitkara University
B.E., Computer Science
January 1, 2015 – January 1, 2019
New Yashoda Public School
12, Non-Medical
January 1, 2001 – January 1, 2014
AIRA Matrix Private Limited
Lead AI Engineer
June 1, 2023 – Present
Mumbai, Maharashtra, India · On-site
EagleView
Data Scientist II
April 1, 2021 – June 1, 2023
Bengaluru, Karnataka, India
SigTuple
Data Scientist I
August 1, 2020 – February 1, 2021
Bengaluru, Karnataka, India
Charmboard
Data Scientist
November 1, 2019 – August 1, 2020
Bengaluru, Karnataka, India
Ocwen Financial Corporation - US
Data Analyst Intern
September 1, 2018 – September 1, 2019
Bengaluru, Karnataka, India
Credible India
Machine Learning Engineer Intern
June 1, 2018 – August 1, 2018
Remote
Face Mask Detection
January 1, 2021 – March 1, 2021
An approach to detecting face masks in crowded places built using RetinaNet Face for face mask detection and Xception network for classification.
Single Shot Multibox Object Detector
January 1, 2021 – March 1, 2021
- A TensorFlow implementation of object detection paper: SSD - Single Shot MultiBox Detector. - Replaced the existing VGG backbone with DenseNet to achieve better performance. - Trained the model on the SKU110K dataset consisting of 12k grocery store images with each image having 200 objects on average, often appearing similar or identical and positioned in close proximity.
DeepLab V3+ for Semantic Segmentation
January 1, 2021 – February 1, 2021
- A Tensorflow2.x implementation of DeepLabV3+ - Clothes extraction with the help of Sematic Segmentation.
Super Resolution GAN
January 1, 2021 – February 1, 2021
Implemented state of the art SRGAN for generating high resolution images from low resolution images.
Xception
January 1, 2021 – February 1, 2021
A TensorFlow2.0 implementation of Xception paper https://arxiv.org/abs/1610.02357
Wasserstein GAN
November 1, 2020 – December 1, 2020
A Tensorflow implementation of W-GAN (https://arxiv.org/abs/1701.07875)
Squeeze and Excitation Networks
October 1, 2020 – November 1, 2020
A Tensorflow 2.0 implementation of Squeeze-and-Excitation Networks (SENet ResNet50)
Text Similarity Using Siamese Network via LSTM and 1D CNN
May 1, 2020 – June 1, 2020
- Implemented and trained a Siamese network to detect question pairs with similar intent. - Optimized the model with contrastive loss, such that embedding of questions with the same intent are closer to each other, while a margin separates those with different intent.
Face Generation with VAE
January 1, 2020 – February 1, 2020
Face Generation with VAE - Implemented state of the art Variational Autoencoder for generating new faces. - Used pretrained ResNet152 to encode image features and then designed a decoder with stacked transpose convolutions to reconstruct the face from latent features.
Multi Layer Neural Network
February 1, 2019 – March 1, 2019
Neural Network (MLP) from scratch using Numpy.
U-Net
January 1, 2019 – May 1, 2019
- A Keras Implementation of U-Net: Convolutional Networks for Biomedical Image Segmentation. - Trained on data science bowl 2018 to segment cellular nuclei.
Face Generation using VAE
January 1, 2019 – February 1, 2019
Variational Auto Encoder, a generative neural network is implemented with TF2.0.
Image Caption Generator using CNN-RNN
January 1, 2019 – February 1, 2019
- Implemented and trained a seq2seq model on the Flickr8k dataset consisting of 8k images comprising five unique title descriptions. - Using pre-trained InceptionV3 as encoder to extract visual features from the image and word2vec for word embeddings to decode captions from the image.
Neural Networks And Deep Learning
Coursera
June 25, 2026 – Present
Blockchain and Deep Learning: Future of AI
Udemy
June 25, 2026 – Present
Python Data Structures
Coursera
June 25, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 25, 2026 – Present
Programming for Everybody (Getting Started with Python)
Coursera
June 25, 2026 – Present
Intro to Python for Data Science
DataCamp
June 25, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 25, 2026 – Present
Python-Build A Virtual Assistant
Udemy
June 25, 2026 – Present
C++
Sololearn
June 25, 2026 – Present
Python 3 Tutorial Course
Sololearn
June 25, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a strong focus on deep learning and computer vision, which aligns well with an ML Engineer role. The diversity of projects, from medical imaging to fashion and general object detection, indicates adaptability and a broad interest within the ML domain. However, the lack of explicit team collaboration or leadership examples in project descriptions, beyond 'Lead AI Engineer' title, limits the assessment of cultural fit. The projects are primarily personal or technical implementations, not necessarily reflecting collaborative work environments.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving approach and an ability to work on complex, real-world challenges. The experience at EagleView and SigTuple highlights operational awareness through model optimization for deployment and inference time reduction. However, without specific psychometric test results or interview data, a comprehensive assessment of soft skills and operational fit is limited.