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Senior Machine Learning Engineer at Adobe
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
North Carolina State University
Master's degree, Computer Science
January 1, 2016 – January 1, 2018
Dwarkadas J. Sanghvi College of Engineering
Bachelor of Engineering (B.E.), Computer Engineering
January 1, 2012 – January 1, 2016
Adobe
Sr. Machine Learning Engineer
October 1, 2024 – Present
Seattle, Washington, United States
Amazon Web Services (AWS)
Sr. Machine Learning Engineer
May 1, 2024 – October 1, 2024
Seattle, Washington, United States
Amazon
Sr. Machine Learning Engineer
December 1, 2023 – May 1, 2024
Seattle, Washington, United States
Amazon
Software Development Engineer II
June 1, 2020 – November 1, 2023
Seattle, Washington, United States
Adobe
Software Engineer, Data
July 1, 2018 – June 1, 2020
Lehi, UT, United States
Lenovo
Machine Learning Engineer Intern
June 1, 2017 – May 1, 2018
Raleigh-Durham, North Carolina Area
Sprint Analytics Bot
September 1, 2017 – December 1, 2017
Deployed a Slack bot for conducting and analyzing asynchronous sprint meetings, using RESTful APIs. Generated visualizations like burndown chart, velocity graphs and also offered recommendations for better sprint planning. Technologies: Node JS, REST, Python, MongoDB, Plotly
Twitter Sentiment Analysis using Apache Spark Streaming APIs and Python
January 1, 2017 – Present
Processed live Twitter data stream using Spark’s streaming APIs, Python and Apache Kafka as a data queuing service. Implemented basic sentiment analysis of the live data stream.
Predicting Bitcoin Price Variations using Bayesian Regression
January 1, 2017 – February 1, 2018
Predicted the price variations of bitcoin, a virtual cryptographic currency, in order to use it as a foundation for bitcoin trading strategy. Used Bayesian Regression and obtained an MSE of 0
Supervised Learning Techniques for Sentiment Analysis
January 1, 2017 – February 1, 2017
Performed sentiment analysis on Twitter and IMDB datasets. Built feature vectors using NLP techniques and doc2vec package. Achieved an average accuracy of 70% using Logistic Regression and Naïve Bayes classifiers. Implementation in Python.
Music Recommender System using Apache Spark and Python
December 1, 2016 – January 1, 2017
Developed a music recommender system using collaborative filtering technique. Implemented in PySpark
Predicting Business Value for Red Hat
September 1, 2016 – December 1, 2016
Applied various machine learning algorithms for classification like Logistic Regression, SVM, Decision Trees on data provided by Red Hat, to predict potential customers for the organization. Tackled challenges like high dimensionality and cardinality of dataset and variables, working with masked variables, etc. by preprocessing the data. Implementation done in R.
Hybrid Approach for Detection of Plagiarism using Natural Language Processing and Text Mining
July 1, 2015 – March 1, 2016
Developed a system to overcome the shortcomings of commercial plagiarism detection tools, such as failure to detect plagiarism when grammatical construct of the sentence is changed or when words are replaced by their synonyms. Correctly detected 80% plagiarism on average when run on 3 sample texts, where traditional tools detected 76% plagiarism. Implementation done in Python using Natural Language Toolkit package.
Machine Learning by Stanford University
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest in data science and machine learning, with a variety of personal projects exploring different algorithms and applications. This indicates a self-driven individual passionate about the field. The experience at large, fast-paced companies like Amazon and Adobe suggests an ability to thrive in demanding environments. The target role of 'Data Analyst' is a slight mismatch with the candidate's 'Sr. Machine Learning Engineer' experience, which is more specialized and advanced. While the skills are transferable, the role alignment needs clarification.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to tackle complex problems and implement solutions, suggesting strong problem-solving and execution skills. The diversity of projects implies adaptability and a proactive approach to learning new technologies. However, without direct assessment data, specific soft skills like teamwork, leadership, or communication in a collaborative setting cannot be definitively evaluated.