Senior Software Engineer | Microsoft
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The University of Texas at Dallas
Master’s Degree, Computer Science
January 1, 2016 – January 1, 2018
Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science & Technology, Chennai
Bachelor of Technology (B.Tech.), Computer Science
January 1, 2009 – January 1, 2013
Microsoft
Senior Software Engineer
March 1, 2025 – Present
Redmond, Washington, United States · Hybrid
Microsoft
Software Engineer II
December 1, 2023 – March 1, 2025
Redmond, Washington, United States · Hybrid
Amazon
Software Development Engineer II
September 1, 2022 – November 1, 2023
Seattle, Washington, United States · Hybrid
McDonald's
Software Engineer II
June 1, 2021 – September 1, 2022
Wayfair
Software Engineer
February 1, 2019 – May 1, 2021
Greater Boston Area · On-site
Micro Focus
SDE Intern(Graduate), Machine Learning
June 1, 2018 – December 1, 2018
Fort Collins, Colorado Area · On-site
Toovio
Machine Learning Engineer Intern
December 1, 2017 – April 1, 2018
Dallas/Fort Worth Area · Remote
The University of Texas at Dallas
Grader
September 1, 2017 – December 1, 2017
Dallas/Fort Worth Area
The University of Texas at Dallas
Instructor at CS Outreach
August 1, 2016 – August 1, 2017
Dallas/Fort Worth Area
Wipro
Project Engineer
November 1, 2013 – June 1, 2016
Chennai · On-site
Dog Breed Classifier
March 1, 2018 – Present
• Built an algorithm to identify canine breed given an image of a dog. If given image of a human, the algorithm identifies a resembling dog breed. • Created a CNN by applying transfer learning on InceptionV3 architecture. • Achieved an accuracy of 82.17% validating the test set. Technologies: Keras(Tensorflow backend), Python, OpenCV Numpy, Pandas
Finding Donors for CharityML
February 1, 2018 – Present
• Investigated factors that affect the likelihood of charity donations being made based on real census data. • Developed a naive classifier to compare testing results. • Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. • Selected the best model based on accuracy, a modified F-scoring metric, and algorithm efficiency. Technologies: Python, Scikit-learn, Seaborn, Numpy, Pandas, Matplotlib
Creating Customer Segments
February 1, 2018 – Present
• Reviewed unstructured data to understand the patterns and natural categories that the data fits into. • Used multiple algorithms and both empirically and theoretically compared and contrasted their results. • Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis. Technologies: Python, Scikit-learn, Seaborn, Numpy, Pandas, Matplotlib
Predicting Boston Housing Prices
January 1, 2018 – Present
• Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. • Identified the best price that a client can sell their house utilizing machine learning. Technologies: Python, Scikit-learn, Seaborn, Numpy, Pandas, Matplotlib
Semantic Search Engine
October 1, 2017 – December 1, 2017
• Architected a deep NLP pipeline with many features like lemmatization, stemming, POS tags, head words, WordNet features using NLTK, Stanford Dependency Parser. • Indexed using SOLR with PySOLR wrapper for better and faster semantic-based data retrieval using cosine similarity. • Achieved an accuracy of 63% calculated using Mean Reciprocal Rank (MRR) by reducing the errors by 6% with the help of feature weights. Technologies: Python, NLTK, Solr, PySolr, Stanford Core NLP Parser, Dependency Parser
Morris Variant D
June 1, 2017 – August 1, 2017
• Developed Morris Variant D, a variant of Nine Men Morris game in Python. • Optimized the static estimation function to predict the best next move from the game tree up to n levels deeps. • Pruned the game tree by Alpha-Beta pruning to reduce the game time by reducing the positions evaluated. Technologies: Python
Predicting Winner of Soccer Match
March 1, 2017 – May 1, 2017
• Developed a predictive model to predict the winner of the soccer match in Spain LIGA BBVA. • Performed data cleansing, identified the correlation between attributes and compared the accuracy achieved by classifiers such as Support Vector Machines(SVM), Naïve Bayes, Logistic Regression and K-Nearest neighbors. • Achieved a prediction accuracy rate of 62.5%. Technologies: Python, Scikit-Learn
DavisBase - Low Level Database Project
March 1, 2017 – April 1, 2017
• Created rudimentary database engine that is loosely based on a hybrid between MySQL and SQLite called DavisBase. • Implemented a file-per-table approach to physical storage and each table file will be subdivided into logical sections of fixed equal size call pages (512 bytes). Technologies: Java
Text Classification
March 1, 2017 – Present
• Implemented Naive Bayes algorithm and Logistic Regression algorithm to classify the emails as ham or spam. • Improved the accuracy by eliminating stop words. Accuracy: 95.18%
Auction Software - Android App
February 1, 2017 – March 1, 2017
• Developed an Android App for an Auction System. • Implemented features: Buyer/Seller Registration, Login, Homepage, Product Insertion/Deletion. Technologies: Android and SQLite
Library Management System
January 1, 2017 – February 1, 2017
• Built a MySQL based application designed from the perspective of a librarian. • Implemented features: Borrower Management, Book Search & Availability, Book Loans and Fine Management. Technologies: Java Swings and MySQL.
Bidding Web Application
December 1, 2016 – Present
• Created an online auction and shopping website where people and businesses buy and sell a broad variety of goods and services. Technologies: Bootstrap, REST Web Services, Hibernate Framework, JavaScript, jQuery.
eWasteBin - Android App
April 1, 2013 – Present
• Developed a location based mobile application to effectively monitor the clearance of waste bins. • Extended the functionality to monitor other projects carried out by Chennai Corporation. • Built a tool for accessing Operational document and knowledgebase articles on the intranet. Technologies: Android, Open Street Maps, Google Location API
AWS Certified Developer - Associate
Amazon Web Services (AWS)
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse project portfolio, ranging from mobile app development to advanced machine learning, indicating adaptability and a broad interest in technology. The progression through roles at Amazon and Microsoft, coupled with ML-focused internships, shows a clear career trajectory towards specialized technical roles. The personal projects align well with the target ML Engineer role, demonstrating initiative and passion for the field outside of formal employment. The breadth of skills across Java, Python, and various ML frameworks suggests a willingness to learn and apply new technologies.
Soft Skills & Operational Fit
The candidate's resume indicates experience in mentoring junior engineers and participating in full development cycles, suggesting good collaboration and leadership potential. Project descriptions are clear and highlight problem-solving approaches. The 'Grader' and 'Instructor' roles at UTD also suggest strong communication and teaching abilities.