Data Infrastructure at LinkedIn | Ex-Adobe | IIIT Allahabad
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I am a Technology Enthusiast with a demonstrated history of working in the software development industry. During my career, I have got the opportunity to explore many different fields such as Machine Learning, Web and Mobile App Development, Big Data and Data Infrastructure. Skilled in Java, Python, Swift, JavaScript, C++. Have worked on frameworks such as Java Play, Restli, Guice, React, Redux, Tensorflow, Hadoop, Spark. I enjoy working in a team and I'm always looking forward to new learning experiences!
Indian Institute of Information Technology, Allahabad
Bachelor of Technology - B.Tech, Information Technology
January 1, 2015 – January 1, 2019
Staff Software Engineer
April 1, 2024 – Present
Senior Software Engineer
March 1, 2022 – April 1, 2024
Software Engineer
February 1, 2020 – March 1, 2022
Adobe
Member Of Technical Staff
July 1, 2019 – February 1, 2020
Adobe
Product Development Intern
January 1, 2019 – June 1, 2019
Human Face Recognition
February 1, 2018 – Present
Mentor: Prof. G. C. Nandi The project involved detecting and recognizing human faces with the help of PCA and LDA techniques. Imposter detection was also handled.
Human Action Detection in Multi-Camera Environment
February 1, 2018 – April 1, 2018
Mentor: Prof. Anupam Agarwal The project involved developing a robust system which effectively and efficiently utilizes multiple cameras to detect human action. In this project, I explored and applied several unsupervised and reinforcement learning algorithms to dynamically select the best camera from the available network of cameras whose view and feed should be utilized to make the decision about which action is being performed currently. Making decision from a single view reduced the computational complexity to a large extent while not compromising the classification accuracy. In the end, 93.1% accuracy was achieved on IXMAS dataset ( 13 actions and 5 cameras ) maintained by INRIA LABS.
Hindi To English Parallel Corpus Generation
August 1, 2017 – November 1, 2017
Mentor: Prof. U. S. Tiwary The aim of the project was to generate parallel corpus i.e. mapping of English and Hindi texts depicting same context and meaning in Indian Judicial Domain Data which can be used further to develop efficient Machine Translation System.
Blogger : Simple Blog Android Application
June 1, 2017 – Present
Developed a blog app which allows user to post about different topics and view posts of other user. • Implemented using Google Firebase platform - the app uses Realtime Database to store and sync app data, Cloud Storage to store articles and images and Authentication system for user sing in.
Car Rental System
October 1, 2016 – November 1, 2016
Developed an application which enables users to book cars according to various criteria available. It manages all the cars, reservations,drivers, other employees and provides facilities like booking, cancellation, billing, adding / removing new cars, drivers etc..
Neural Networks and Deep Learning
Coursera
June 23, 2026 – Present
Applied Machine Learning in Python
Coursera
June 23, 2026 – Present
Cultural Fit Analysis
The candidate's project history shows a strong academic foundation in Machine Learning, which aligns with an ML Engineer role. Their professional experience at LinkedIn in data infrastructure, while not explicitly ML engineering, provides a solid foundation in handling large datasets crucial for ML. The progression from Software Engineer to Staff Software Engineer at a major tech company like LinkedIn suggests a drive for growth and impact. However, the direct application of ML in their professional roles is not explicitly detailed, which might require further investigation to assess the depth of their practical ML engineering experience beyond academic projects.
Soft Skills & Operational Fit
The candidate's progression to Staff Software Engineer at LinkedIn indicates strong leadership, problem-solving, and collaboration skills. The project descriptions suggest an ability to work on complex, research-oriented tasks and deliver measurable results (e.g., 93.1% accuracy on IXMAS dataset). The focus on data infrastructure aligns well with operational needs for robust ML systems.