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Ajay Kumar Chhimpa

Software Engineer

Bengaluru, India

linkedin.com/in/ajaychhimpa1

+91-8058888056

ajaychhimpa1@gmail.com

RELEVANT EXPERIENCE

Software Engineer LinkedIn, India Aug 2021 - Present

• Increased purger pipeline (pipeline to make data GDPR compliant) coverage from 87% to 100% by

analyzing, categorizing and fixing pipeline issues.

Software Development Engineer CommerceIQ, India Nov 2019 - June 2021

• Introduced proxies while downloading reports (pipeline which downloads reports on daily basis), which

resulted in reducing pipeline completion time from average 5 hours to less than 2 hours also improving

report download success rate from average 80% to 100%.

• Improved and fully automated new client setup process by developing microservice to fetch multifactor

authentication details from amazon, reducing new client setup time from average 2 hours to less than 10

minutes.

• Optimized internal tool - Modulus, by introducing git mirroring on the server to avoid remote git calls,

optimized two frequently used api’s and reduced their average response time by 80% (44 to 9 seconds) and

60% (18 to 7 seconds).

Software Engineer Milvik Technologies, India July 2018 - Nov 2019

• Reduced time required for real-time migration of new data from average an hour to less than a minute by

modifying open-source Maxwell (acts as a producer for Kafka) to create dynamic topics for Kafka on the

basis of type of query (insert, update, delete).

TECHNICAL SKILLS

• Programming Languages: Java, Python (basic), C++ (basic)

• Databases: MySQL, PostgreSQL (basic), Neo4j (basic)

• Operating Systems: Mac OS, Linux, Windows

• Frameworks and tools: Spring, Hibernate, Intellij IDEA

• Others: Git, Maven, Kafka, Redis, New Relic, Jira

EDUCATION

Patiala, India Thapar Institute of Engineering & Technology July 2014 - May 2018

• B.E. in Computer Engineering. CGPA: 7.77/10

• Related Courses/Certificates: Data Structures and Algorithms, Introduction to Java programming, Natural

Language Processing, Deep Learning Specialization

PROJECTS

• Handwritten Digit Recognition using CNN: A Convolutional neural network model in TensorFlow for

handwritten digit recognition using MNIST data. Used two convolutional layers with max-pooling, two fully

connected layers, Adam optimization algorithm, and dropout for regularization achieving an accuracy of

around 99.4%.

• Fake News Classification: Used Naïve Bayes and SVM algorithms to classify news articles as fake/real. F1

score and accuracy of both were around 96% on test data. Implemented in python using nltk, sklearn and

numpy modules.

EXTRACURRICULAR/INTERESTS

• Ranked in top 5 in various programming contests held in our university.

• Actively volunteered for 1 year at “Pratigya”, an NGO working towards the education of underprivileged

children.

• Machine Learning, Natural Language Processing