Ajinkya Ghadge

Ajinkya Ghadge

Masters in Computer Science at UMass. Software Engineer

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Resume

1040 North Pleasant Street
264 Puffton Village Apartments
Amherst, MA, 01002

AJINKYA GHADGE

( 413 ) 522 - 5217
aghadge@umass.edu
https://ajinkyaghadge.github.io

EDUCATION
Amherst, MA University of Massachusetts Amherst Expected graduation: May 2021
Master of Science in Computer Science, GPA 3. 86 /4.
  • Coursework: Database Design and Implementation, Operating Systems, Machine learning , Neural Networks, Applied Statistics, Machine learning in the Real World, Software Engineering , Secure Distributed Systems

Kolhapur, India Shivaji University^ Jul 2012 – May 2016 Bachelor of Technology in Computer Science and Engineering, GPA 3.86/4.

EXPERIENCE

Pune, India (^) Persistent Systems Nov 2016 – Feb 2018 Software Engineer

  • Developed Java CLI tool used by 20 + people to orchestrate real-world FOREX transactions
  • Improved fault tolerance and scalability by migrating existing XML over HTTPS inter-process communication to Message Queue in Java for large amount of transactional data
  • Refactored , profiled and analysed code for ~ 4 x faster execution by interfaceing Python/C using Ctypes
  • Migrated legacy C methods to Python and collaborated with delivery team to draft updated documentation
  • Achieved ~ 3 x faster execution of scripts by refactoring Java code to run concurrently on distributed system
  • Spearheaded initiative to prototype highly automated and integrated Full-Stack regression testing using Java, Selenium, Jenkins, Appium, RestAssured, Junit for better reporting and CI/CD migration with 4 peers
  • Collaborated with 2 other teams on tools development and component integration over daily scrum meeting
  • Volunteered and trained 2 new team members in product, domain knowledge and weekly team workflow
TECHNICAL EXPERIENCE
Projects
  • Event stream processing to find and explain anamolous behavior in Hadoop cluster (Jan 2020 – April 2020)
    • Implemented Complex Event Processing to determine events causing cluster imbalance, bottlenecks and faults by analysis of data generated in hadoop cluster logs for 3 different workloads
    • Transformed logs to 1200 + attributes time-series data to implement algorithm determining the cluster faults and reduced number of attributes for faulty explanation by 90.5 % , reducing fault detection time
  • Dataset generation pipeline from raw data for Machine learning Inference (March 2018 – July 2019 )
    • Applied threaded polling and memory mapping to improve image frame capture to 200 + frames per seconds
    • Implemented python script to generate ~ 10 TB of Infrared and RGB image datasets using multiprocessing and binary threshold
    • Built RESTful Web Application using the Python-Flask framework for deploying machine learning model
  • Scenery classification using TF-IDF, Scene Parsing and Natural Language Processing (Oct 2019 – Dec 2019)
    • Compared information retrieval methods, word embeddings, and Neural networks for mapping correlation between object labels and scenes for refining classification accuracy of scenes by 82 %
AWARD(S) AND VOLUNTEER WORK
  • Certificate of Merit and Scholarship (Academic Year 2013 - 2014 )
Languages and Technologies
  • Python ( 4 years); Java( 4 years); C; SQL; JavaScript; SciKit; Pytorch; Numpy; PostgreSQL -^ Visual Studio; Microsoft SQL Server; Eclipse; Google Cloud Platform, AWS EC^2 , Hadoop^