Senior Software Engineer at Meta| Ex Google| Ex Amazon
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
New York University
Master of Science (M.S.), Computer Science
January 1, 2016 – January 1, 2018
Manipal Institute of Technology
Bachelor of Engineering (BE), Electronics and Communications Engineering
January 1, 2009 – January 1, 2013
Meta
Senior Software Engineer
November 1, 2024 – Present
Menlo Park, California, United States
Adobe
Senior Software Development Engineer
May 1, 2023 – October 1, 2024
United States
Software Engineer
February 1, 2019 – April 1, 2023
Mountain View, California
Audible, Inc.
Software Development Engineer
June 1, 2018 – February 1, 2019
Newark, New Jersey
Behold.ai
Machine Learning Intern
January 1, 2018 – May 1, 2018
Greater New York City Area
VT iDirect
Software Developer Intern
May 1, 2017 – August 1, 2017
Virginia, United States
Samsung R&D India
Senior Software Engineer
April 1, 2015 – August 1, 2016
Bangalore
Samsung R&D Institute India
Software Engineer
July 1, 2013 – March 1, 2015
Bengaluru, Karnataka, India
Ingersoll Rand Security Technologies
Intern
January 1, 2013 – July 1, 2013
Think And Learn
Intern
May 1, 2012 – July 1, 2012
Bengaluru , India
R & D Centre for Iron & Steel, Steel Authority of India Ltd.
Intern
June 1, 2011 – July 1, 2011
Predicting star reviews & extracting tips from YELP reviews
April 1, 2017 – May 1, 2017
Developed a model to predict the star ratings of Yelp Reviews for restaurants using various machine learning algorithms such as Naïve Bayes, Bag of words, SVM etc. Also, explored the effects of various refining features such as adding n-grams, stemming words and removing stop words for each classifier, tf-idf weighing.
Two Pass Linker, CPU Process Scheduler, IO Scheduler
February 1, 2017 – April 1, 2017
Technologies Used: C++ STL Implemented a two-pass linker, analyzed, and calculated the various addressing modes for different inputs. Also, Implemented FCFS, LCFS, SJFS, Round Robin, Priority Scheduling, Page Replacement and Resource Allocation algorithms
Part of Speech, Chunk and Name Tagger
February 1, 2017 – April 1, 2017
Technologies Used: Java, Open NLP Developed a tagger that analyzes a corpus of words and tags each word with its most probable POS, Chunk and Name tags. Implemented using Viterbi algorithm and Maximum Entropy Markov Model.
PATH (Port Authority of New York & New Jersey) Mobile App
October 1, 2016 – December 1, 2016
The App allows users to quickly and easily access timetables for any PATH route along with PATH maps. It uses PATH’s official alerts RSS feed to provide real-time updates to PATH users. Technologies Used: Node.js, MongoDB, PhoneGap
Airline Delay Prediction & Airline Rating Model
October 1, 2016 – December 1, 2016
Implemented an airline delay prediction model using predictive analysis and developed an airline rating system based on user review points, price and delay. User review points were obtained by performing sentimental analysis on the twitter feeds for each airline Technologies Used: Hadoop MapReduce, Spark MLLib, Hive
Cultural Fit Analysis
The candidate has worked at several large, well-known tech companies (Meta, Adobe, Google, Samsung, Audible), suggesting an ability to adapt to diverse corporate cultures. The range of projects, from operating systems concepts to mobile app development and machine learning, indicates a broad technical interest. However, the target role is 'ML Engineer', and while there are ML projects, the majority of professional experience is in general software development. This might indicate a need for further assessment of their commitment and depth in ML-specific roles to ensure a strong cultural fit for a dedicated ML engineering team.
Soft Skills & Operational Fit
The candidate's resume indicates a strong background in software development and a transition towards machine learning. The project descriptions suggest an ability to work on complex technical problems. However, without psychometric or English test scores, it's difficult to assess communication clarity, work attitude, stress handling, or team collaboration directly. The detailed project descriptions imply good technical communication.