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Developer with 10 years of experience and a background in Computer Science and Machine Learning. I love solving problems that are at the intersection of the two - ⇒ As a Data Scientist, I've been involved in ideating, building and scaling products that use various Machine Learning/Statistical Modeling techniques and evangelized business insights to the customers. ⇒ As a Software Engineer, I've been involved in developing large scale distributed systems. I challenge myself by participating in various Data Science and Coding competitions. I Currently rank in top 50 at Analytics Vidhya. Passionate to be part of generative AI revolution. “””The horse that follows the herd may keep pace, but the one that charts its own course achieves exponential triumph.”””
VNR Vignana Jyothi Institute of Engineering and Technology (VNRVJIET)
Bachelor of Technology (BTech), Computer Science
January 1, 2012 – January 1, 2016
Stealth Startup
Founder
March 1, 2026 – Present
Face Generation Using GAN
October 1, 2017 – Present
I used generative adversarial networks to generate new images of faces by using Celeba dataset. I used Deconvolution at generator and applied batch normalization for generating images and used Convolution at discriminator for Classifying generated image.
Generate TV Scripts
October 1, 2017 – Present
In this project I generated Simpsons TV scripts using RNNs. Neural Network we build will generate a new TV script for a scene at Moe's Tavern. I applied embedding with 300 dimensions to input_data and used two LSTM layers with 256 cells to the embedding output using TensorFlow.
Language Translation
October 1, 2017 – Present
I trained a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French. Implemented Encoder RNN layer and Decoder RNN layer. I used 2 LSTM layers with 256 cell on 300 dimensions embedded input. Applied Sentence to sequence using sequence to sequence model.
Image Classification of CIFAR-10 dataset using CNN
September 1, 2017 – Present
Classified images from the CIFAR-10 dataset. Used 3 convolutional layers, 2 fully connected layers and one final output layer. Achieved 70% accuracy on test set which is 7 times better than random guess (10%)
Deep Learning Specialization
Udacity
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Python for Everybody Specialization
Coursera
June 24, 2026 – Present
Machine learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a strong focus on deep learning and machine learning, aligning well with an ML Engineer role. The diversity of projects (image generation, text generation, translation, classification) indicates a broad interest within the ML domain. However, the lack of team-based projects or professional experience makes it difficult to assess collaboration or broader cultural fit.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's experience as 'Founder' in a 'Stealth Startup' is noted but lacks details for evaluation.