Research Fellow @ Microsoft Research | GSoC’24 @ML4SCI | Kaggle 4x Expert | Ex-Intern @ Sony Research India
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Research intern at Microsoft Research working on LLM reasoning.
Indian Institute of Technology (Indian School of Mines), Dhanbad
Integrated Masters of Technology, Mathematics and computing
January 1, 2021 – January 1, 2026
Christ University, Bangalore
PUC, PCMC
August 1, 2019 – August 1, 2021
Microsoft
Research Intern
November 1, 2025 – May 1, 2026
Bengaluru · On-site
Sony Research India
Research Intern
June 1, 2025 – October 1, 2025
Bengaluru · Remote
Swiggy
Data Scientist
July 1, 2024 – May 1, 2025
Bengaluru, Karnataka, India · Remote
Google Summer of Code
Contributor @ ML4SCI
May 1, 2024 – October 1, 2024
Remote
Nyun AI
Research Intern
May 1, 2024 – June 1, 2024
Bengaluru, Karnataka, India · Remote
Bosch Global Software Technologies
Computer Vision Researcher
January 1, 2024 – May 1, 2024
Bengaluru, Karnataka, India · Hybrid
UiT- The Arctic University of Norway
Machine Learning Researcher
November 1, 2023 – January 1, 2024
Bengaluru, Karnataka, India · Remote
SimplyFI Softech India Pvt. Ltd.
AI/ML Engineer
July 1, 2023 – November 1, 2023
Bengaluru, Karnataka, India · Hybrid
PatilKaki
AI/ML Engineer
July 1, 2023 – August 1, 2023
Bengaluru, Karnataka, India · Remote
Culinda Inc.,
AI/ML Engineer
April 1, 2023 – June 1, 2023
Hyderabad, Telangana, India · Remote
Kaggle
Kaggle 4x Expert
June 1, 2022 – April 1, 2026
Remote
CyberLabs IITISM
Student Researcher
May 1, 2022 – March 1, 2026
Dhanbad, Jharkhand, India
AMD AI Premier League
July 1, 2025 – July 1, 2025
The AMD-AIPL project was developed for a hackathon organized by AMD at IISc, where we built two AI agents: a Q-Agent that generates logic-based multiple-choice questions from given topics, and an A-Agent that answers them with reasoning. The competition followed a cricket-style 1v1 format with innings and scoring based on question quality and answer accuracy. We fine-tuned both agents using supervised learning and Generative Reinforcement with Policy Optimization (GRPO), implemented a self-play mechanism for iterative improvement, and crafted custom prompts to enhance logical consistency. Strict constraints were followed regarding model usage, format, inference time, and token limits, using the Qwen3-4B model for both agents.
Convolutional Neural Networks
Coursera
June 25, 2026 – Present
Improving Deep Neural Networks : Hyperparameter Tuning Regularization and Optimization.
Coursera
June 25, 2026 – Present
Machine Learning
Coursera
June 25, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 25, 2026 – Present
Cultural Fit Analysis
The candidate's experience spans various domains (e-commerce, scientific research, autonomous driving, document AI) and includes contributions to open-source (GSoC) and competitive platforms (Kaggle). This diversity suggests an adaptable individual who can thrive in different technical cultures. The focus on cutting-edge AI/ML research and application aligns well with an innovative, fast-paced environment. However, the lack of explicit team-based project descriptions limits a deeper cultural fit assessment.
Soft Skills & Operational Fit
The candidate's diverse project portfolio and multiple research internships suggest strong problem-solving abilities, adaptability, and a proactive learning attitude. The descriptions indicate a capacity for independent research and collaboration within structured environments. However, direct assessment of communication, teamwork, and stress handling is not possible without psychometric or interview data.