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Deep Learning | Computer Vision | RecSys | PyTorch | ONNX
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Assessing your cultural and operational fit
Former Applied Scientist at Amazon, working on Computer Vision.
New York University
Master of Science (M.S.), Computer Science
January 1, 2014 – May 1, 2016
Maulana Azad National Institute of Technology
B. Tech, Computer Science
January 1, 2007 – January 1, 2011
The Walt Disney Company
Senior Machine Learning Engineer
April 1, 2025 – Present
Seattle, Washington, United States · On-site
OpenSpace
Senior Machine Learning Engineer
April 1, 2024 – March 1, 2025
Seattle, Washington, United States · Remote
IUNU
Machine Learning Engineer
April 1, 2023 – April 1, 2024
Seattle, Washington, United States · Hybrid
Naska.AI
Senior Machine Learning Engineer
February 1, 2022 – April 1, 2023
Remote
International Institute of Information Technology Bangalore
Research Associate
February 1, 2020 – February 1, 2022
Bengaluru, Karnataka, India
Amazon
Applied Scientist
August 1, 2017 – October 1, 2019
Greater Seattle Area
Universal Robotics
Machine Learning Engineer
August 1, 2016 – August 1, 2017
Nashville Metropolitan Area
New York University
ML Research Assistant
May 1, 2015 – August 1, 2015
New York University
Graduate Student
August 1, 2014 – May 1, 2016
SAP Labs
Software Developer
August 1, 2011 – June 1, 2014
Bengaluru
National Institute of Oceanography
Research Intern
May 1, 2010 – June 1, 2010
Indian Institute of Scientific and Engineering Research, Kolkata
Intern
November 1, 2009 – December 1, 2009
Compiler for a typed dialect of Javascript
January 1, 2016 – May 1, 2016
Done as a part of the Compiler Construction Capstone course at NYU http://cs.nyu.edu/courses/spring16/CSCI-GA.2130-001/ There were three project milestones: 1) Parser and Syntax analysis 2) Type checker and semantic analysis 3) MinARM code generator. MinARM is a dialect of ARM with fewer instructions than the full-blown ARM instruction set. For the project we used HACS: https://github.com/crsx/hacs
Bayesian Optmization Library using Gaussian processes for sklearn
November 1, 2015 – Present
This is a library for doing hyperparameter tuning based on GP-based Bayesian Optimization in Machine Learning models, using the popular library scikit-learn. Current features: Does sequential Bayesian Optimization using GPs Easy to use and implement for current scikit-learn users Plots GPs and acquisition functions Acquisition functions implemented include EI, PI, GP-UCB Uses scikit-learn's Gaussian Processes module (which does not include Matern kernel as of now)
Detect extent of Diabetic Retinopathy in a patients eye using Machine Learning
June 1, 2015 – Present
Detect the degree of this degenerative disease that causes blindness in diabetics using Deep Neural Nets that trained on wavelet based features of retinal images. Found wavelet based edge features to have the best accuracy ~79%. Key challenges: Imbalanced data set. Data augmentation techniques increase size of already large data set. Large feature space.
User listening behavior analysis of the Million Song Dataset
January 1, 2015 – March 1, 2015
Test if 1.02 million unique users have a predilection towards songs with similar spectral properties. Our results showed that users listened to songs from similar groups with an accuracy of ~80%. Proposed a generalized approach to such analyses.
Smart Card based User Authentication System For students
January 1, 2011 – Present
A web based access system that uses a smart card based authentication mechanism to maintain student master data. Concept work was done to make various student facilities available using smart cards, like access to the library or student meals around campus.
Supervised Learning With Support Vector Machines
January 1, 2010 – April 1, 2010
A study detailing the use of LibSVM on classifying spam. Various models and kernels for the SVM were implemented in C or C++ and their performance metrics were pitted against each other. Several data sets were used and a simple comparative study was done as to which kernel functions best suited certain data sets.
Cultural Fit Analysis
The candidate's project diversity, ranging from compiler construction to various machine learning applications (healthcare, music, robotics, e-commerce, agriculture), indicates a broad interest and adaptability. The progression through roles at companies like Amazon, Disney, and various ML/AI startups, along with research positions, suggests a drive for innovation and continuous learning. The target role of ML Engineer aligns well with the candidate's career trajectory and demonstrated expertise. The breadth of skills across different ML domains and industries points to a strong cultural fit for dynamic, technically challenging environments.
Soft Skills & Operational Fit
The candidate's resume highlights roles that require problem-solving, optimization, and communication, particularly in research and development settings. The project descriptions indicate an ability to tackle complex challenges and propose generalized approaches. The SAP Labs experience as a 'UI Expert, Performance Expert and Team Architect' suggests leadership and collaborative skills. However, without specific assessment data on soft skills or operational fit, a detailed analysis is limited.