Vrezh Khalatyan

Java Software Engineer

  Glendale, CA, USA

Forward-thinking Software Engineer with background working effectively in dynamic environments. Fluent in Java and Python programming languages. Proud team player focused on achieving project objectives with speed and accuracy. Software Engineer skilled at technical leadership, communication and presentations. Experienced in full project life cycle from design to implementation to integration.


Work Experience

Java Software Engineer  •  Music Reports Inc

December 2020 - Present

  • Developed high-quality software design and architecture utilizing Java.
  • Implemented multi-threading and exception handling to improve application functionality
  • Identified, prioritized, and executed tasks in the software development life cycle.
  • Automated tasks and wrote complex queries to retrieve data using PostgreSQL.

Application Developer  •  City of Los Angeles

June 2018 - November 2020

  • Developed and tested new product offerings prior to release to assist the development team with bug identification.
  • Developed, enhanced, and maintained Bureau intranet and internet web-based business applications, as well as Android mobile applications.
  • Conducted regression testing, analyzed results and submitted observations to the development team.
  • Prepared and submitted reports and other documentation to assist development team members.

Media Technician  •  Glendale Unified School District

October 2016 - June 2018

  • Designed, implemented and monitored web pages and sites for continuous improvement.
  • Created eye catching and functional digital design concepts across various platforms to strengthen the company's brand and identity.
  • Tested websites and performed troubleshooting prior to deployment.

Sales Support Lead  •  Best Buy

June 2006 - October 2016

  • Consulted with sales and management to refine individual and team deliverables; interpret complex analyses and reports, making complex decisions to drive effective business outcomes.


2016 - 2019

California State University

Computer Science


CohibaMate (Cigar Label Authentication) 

CohibaMate utilizes Google's Tensorflow to train a deep convolution neural network on an exclusive data-set. The pre-trained model with a 97% accuracy rate is then used by a Python/ Flask application which allows the user to upload an image of a Cohiba cigar band and have the model predict the image's label as being authentic or counterfeit. 

Available at Google Play Store

Traffic Prediction Application (City of Los Angeles) 

Designed and implemented a data preprocessing pipeline to create the linkage network for the City of Los Angeles and to reasonably impute missing data for any time series. Trained and tested GRNN model to predict the traffic speeds of the streets in the Los Angeles Financial District, and verify the GRNN model and its resulting output using various visualizations. 


  • Java
  • JavaScript
  • JQuery
  • AJAX
  • PHP
  • Python
  • C#
  • C/C++
  • HTML5
  • CSS3
  • Haskell
  • node.js / express.js
  • SQL Analytics
  • Database Design and Development
  • Software Development Fundamentals and Process
  • Software Testing/ Debugging
  • Selenium
  • Appium
  • Machine Learning
  • Big Data
  • Data Science
  • Data and Quantitative Analysis
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