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Fullstack Engineer with 1+ years in Full-stack Development & Machine Learning
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Result-driven Third-year Computer Science undergraduate with experience across full-stack development, and machine learning. Built scalable solutions including OmniBridge, a low-latency integration platform, a concurrent E-Learning Platform, and an Automated Fraud Detection System. Strengthened algorithmic expertise as a Programmer at MMIL, achieved LeetCode Knight status (top 5.68% globally), and ranked among the top 8% contributors in GirlScript Summer of Code.
JSS Academy of Technical Education
Bachelor of Technology · Computer Science and Engineering
N/A – June 30, 2028
Sunbeam School Mughalsarai
Class 12th · CBSE
N/A – May 31, 2024
St. Mary's Convent School
Class 10th · ICSE
N/A – May 31, 2022
Microsoft Mobile Innovation Lab (MMIL)
Programmer
April 1, 2025 – Present
Noida, Uttar Pradesh, India
OmniBridge
February 1, 2026 – March 1, 2026
Architected an integration layer connecting a Single Window System (SWS) prototype across 5+ simulated legacy endpoints, with an event-driven webhook handler processing 500 events per batch. Enhanced API routing logic, reducing request processing time from 8 seconds to under 1.5 seconds.
View ProjectE-Learning Platform
November 1, 2025 – December 1, 2025
Built a full-stack learning platform with role-based dashboards and JWT-based authentication, supporting secure multi-user access across administrators, instructors, and students. Developed course progression tracking and persistent state management, enabling seamless content resumption across sessions. Designed and optimized 8+ RESTful APIs using MongoDB aggregation pipelines, reducing average query response time from 350ms to 140ms.
View ProjectAutomated Fraud Detection System
September 1, 2025 – October 1, 2025
Crafted an end-to-end fraud detection pipeline with a Streamlit dashboard for real-time scoring on 10K+ records, applying SMOTE-based oversampling to improve minority-class recall by 18%. Improved model F1-score by 12% through feature selection and hyperparameter tuning using GridSearchCV.
View ProjectCultural Fit Analysis
The candidate's involvement in Google Women Techmakers and GirlScript Summer of Code indicates a proactive and community-oriented individual, which aligns well with collaborative and inclusive work environments. The diversity of projects (e-learning, fraud detection, integration layer) suggests adaptability and a broad interest in different technical domains. The target role of Fullstack Engineer aligns with the candidate's project experience.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted systems and optimize performance. Collaboration is mentioned in the MMIL experience. The academic achievements and competitive programming suggest a driven and analytical mindset. However, without direct assessment data, specific soft skills like leadership, conflict resolution, or detailed communication style cannot be fully evaluated.