Fullstack Engineer with less than a year in React, Django, and ML Pipelines
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Computer Science graduate with hands-on experience building end-to-end ML pipelines in NLP and Computer Vision. Designed a CNN weed classifier achieving ~99% accuracy and an AI Financial Q&A Assistant with 88-92% intent classification accuracy. Proficient in Python, TensorFlow, and PyTorch, with a track record of reducing latency and improving system performance in production-ready applications.
Aditya Institute of Technology and Management, Tekkali
B.Tech · Computer Science and Engineering
September 1, 2021 – May 1, 2025
Metro Lab Services Pvt. Ltd.
Full Stack Web Development Intern
April 1, 2024 – May 1, 2024
Hyderābād, Telangana, India
AI-Powered Financial Q&A Assistant
March 1, 2026 – Present
Constructed an AI-based Q&A system using LLMs via Ollama to deliver data-driven answers to finance-related queries. Implemented an intent recognition engine combining NLP and Ollama, reaching 88-92% accuracy on a held-out evaluation set. Scaled to 1,000+ daily requests at sub-500ms latency by integrating SQL/NoSQL databases with financial REST APIs. Raised query response relevance by 30% over baseline through text classification and context-aware answer generation. Created an interactive interface to support financial literacy, increasing user task completion rate by 35%.
View ProjectWeed Image Classification Using Deep Learning Framework
December 1, 2025 – December 1, 2025
Developed a CNN-based deep learning framework for early weed detection, targeting low-cost identification in precision farming. Applied image preprocessing to standardize input quality, achieving 99% accuracy and 0.91 F1-score on a stratified train/test split. Structured targeted data augmentation to correct class imbalance, boosting minority-class recall by 22%. Tuned hyperparameters via the Firefly Algorithm metaheuristic, cutting convergence time by 35% over grid search. Shipped an inference system with sub-200ms per-image latency, enabling near-instant weed classification in the field.
Introduction to Machine Learning
IIT Kharagpur
January 1, 2024 – Present
Data Mining
IIT Kharagpur
January 1, 2024 – Present
Python for Data Science
IIT Kharagpur
January 1, 2023 – Present
Database Management System
IIT Kharagpur
January 1, 2023 – Present
Cyber Security Workshop
Supraja Technologies
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a diverse interest in AI/ML applications (finance Q&A, precision farming) and full-stack development. The academic background and multiple NPTEL certifications from IIT Kharagpur indicate a strong drive for continuous learning and self-improvement, which is a positive cultural indicator. The internship, though short, shows an ability to contribute to a production environment. However, the overall experience is heavily academic, and exposure to diverse team structures or corporate culture is limited, which might require some adaptation.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a results-oriented approach, focusing on metrics like accuracy, latency, and user task completion. This indicates a strong operational fit and an ability to deliver measurable impact. The academic projects and certifications suggest a proactive learning attitude. However, with only one short internship, direct experience in team collaboration and handling complex project dynamics in a professional setting is limited.