Hi I am Rohit Jain,
I am a majors in Computer Science from BITS Pilani, India and currently working as a Software Engineer at Rubrik, Bangalore. I have previously worked as an MTS-2 at Nutanix, Bangalore and SDE intern at Samsung Resarch Institute - Bangalore. I have always been enthusiastic about recent trends in Computer Science and keenly interested in the domains of Software Engineering, Distributed Systems, Operating Systems, System Design and Algorithms
I also like to spend my time on solving algorithmic problems on online platforms like codeforces and LeetCode.
I have also done projects in the domains of:
Languages: C++, C, Python, Scala
Frameworks: Pytorch, Tensorflow
Technology: Git, Linux, NFS, iSCSI, NFS
Softwares: IBM SPSS, Wireshark, Postgres
• Engineered a snappable agnostic framework to support backup, disaster recovery and, archival solutions for enterprise and open source databases cutting development times down to 1/4th of the original time. • Added support for Postgres backup and archival on Rubrik Security Cloud enabling the customers to onboard their 100+ Postgres VMs to the unified data management platform.
• Led the redesign of metadata store replacing Cassandra with RocksDB and scaling support for deep storage with read-heavy workflows from 100 physical nodes to more than 300 nodes. • Optimized the iSCSI target digest computation process by introducing a shared locking mechanism resulting in a remarkable decrease in login times and scaling simultaneous logins from 128 targets to 2000+ targets. • Pioneered the implementation of a robust network interface hosting mechanism on multiple nodes, providing multi-path IO services to clients, dramatically reducing iSCSIreconnection latencies from 15s to under 1s. • Won Nutanix hackathon 2023 forinnovating a snapshot chain management solution over S3,reducing the primary cluster storage requirement to 1/10th saving crucial space for other metadata operations. • Led the research paper initiative to foster culture of academic review of innovation in large-scale storage systems.
Assisted over 70+ students in weekly laboratory sessions in learning IBM SPSS and Python for Data Mining techniques. Helped in preparing content for laboratory exercises, framing questions for evaluation components, and evaluating answer sheets for the course.
• Developed an error injection framework forfailing gRPC requests reducing simulation testing times by 80% for a degraded distributed cluster. • Extended the framework for detection of slow nodes with minimal false positives and latency of less than 100ms.
• Created an android application that learns the user color-tone preferences via on-device learning approach. • Trained a GAN-based Model for user behaviorlearning in a few-shot meta-learning scenario, achieving user satisfaction score of 90+.
Assisted over 70+ students in weekly laboratory sessions in learning IBM SPSS and Python for Data Mining techniques. Helped in preparing content for laboratory exercises, framing questions for evaluation components, and evaluating answer sheets for the course.
• Worked under Dr. Bhavana Prasher, Dr. Mitali Mukherjee and Rintu Kutum on entropy-based model for unifying categorical data to perform clustering and other data modeling tasks for 10000+ questionnaire data. • Used divergence model for classification of healthy individuals into different Prakriti classes and achieved an accuracy rate of over 95%
UI/UX team lead responsible for graphic designing for on-campus and off-campus publicity of the TEDx event. Handled the production department, including on day technology and photography.
Filing date: June, 2024 File at: US and India Patent Offices
Patent forimproving temporary write buffer(oplog) flushing, achieving eightfold performance gains by optimizing drain speeds for consistent I/O performance during various states (e.g., idle,rebuild) when serviced by the persistent storage tier or SSD-based oplog.
Publication date: Jan 26, 2020 Published at: PyPi
Implemented the research paper ‘A Unified Entropy-Based Distance Metric for Ordinal-and-Nominal-Attribute Data Clustering’ by Zhang et al. and built a python package for finding a common distance matrix for the ordinal and nominal data from any kind of questionnaire data, based on entropy measures. Tested the package on multiple datasets for robustness.
Source Code: Click here
PyPI Link: Click here
I have completed the following certified courses:
If you would like to collaborate or contact me, drop me an email or message me on linkedin.