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Research Fellow @ Microsoft Research | GSoC’24 @ML4SCI | Kaggle 4x Expert | Ex-Intern @ Sony Research India
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Assessing your cultural and operational fit
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.