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Software Developer Resume

TECHNICAL SKILLS:

Languages: Python, C, Assembly, Java, Java script, R, HTML, JSS, C#, C++

Tools: MATLAB, Visual Studio, Keras, Tensor - Flow

Operating System: Windows, MAC, UNIX, Linux

Sources: GIT, SVN, JIRA and JENKINS repository

WORK EXPERIENCE:

Confidential

Software Developer

Responsibilities:

  • Product owner and Agile Scrum Master for STB simulator named SoftBox.
  • Designed new cookies cache method for Netfront Browser
  • Designed REST API’s for SoftBox products which helps customers to interact with Metadata
  • Implemented VideoScape Open APIs on Node.js server to provide components for CMDC, PPS, UPM, HEP
  • Designed and implemented helper methods to post and pre-process the data from SQL database.
  • Designed and implemented the routing of each REST api with the help of EXPRESS node module

Confidential

Software Developer

Responsibilities:

  • Translated concepts of edge detection, contrasting, blurring and sharpening of images into mathematical algorithms
  • Implemented the algorithm in python using libraries Numpy, Scipy and PIL
  • Quality of Corner/Edge detection in real time is high which makes it suitable for advance application such as “Real-time Face Recognition” or “Real time Surveillance”

Confidential

Software Developer

Responsibilities:

  • Observed the effect of Standard Precipitation Index (SPI) on crop yield in the United States.
  • After cleaning Confidential data using R and using regression concepts, fitted a curve for predicting the crop yield for the known values of SPI.

Confidential

Software Developer

Responsibilities:

  • Development of new pattern matching scheme which involves using DFA and NFA engine for faster lookup and packet forwarding using fragments of Rules set.

Confidential

Software Developer

Responsibilities:

  • Analysed cancer cells tumor using various machine learning models like Logistic regression, SVM, Neural Networks and K-Means

Confidential

Software Developer

Responsibilities:

  • Experimenting Deep vs Wide neural networks in classification of voice files
  • In depth knowledge of regularization L1, L2 and Dropout
  • Hands on knowledge on Keras library, Tensor Flow

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