About
Final-year Computer Science (AI & ML) student specializing in backend development with Python, Django REST Framework, and FastAPI. Experienced in designing secure REST APIs, JWT authentication, role-based access control, and database-driven systems using PostgreSQL and MongoDB. Focused on writing clean, maintainable code with strong exception handling and a debugging-first approach to building reliable backend systems.
Skills & Expertise (39)
Work Experience
Backend Developer
AuthX
Present - Present
Architected a secure RBAC backend using Django REST Framework and PostgreSQL, implementing JWT authentication with access/refresh token flow, email verification, and OTP-based login for privileged roles. Designed and built 15+ REST API endpoints covering registration, login, password reset, role assignment, user management, and audit logging, writing clean, modular, maintainable code. Built a custom permission-based authorization engine enforcing Admin/Manager/User role access at the API level, with object-level ownership checks and audit-log tracking for sensitive actions. Implemented secure logout with refresh-token blacklisting and wrote 8 backend test cases covering authentication, RBAC permissions, and protected routes.
Backend Developer
SmartHire
Present - Present
Designed a secure FastAPI backend with JWT authentication (OAuth2PasswordBearer, Passlib/bcrypt) and MongoDB Atlas integration, powering an AI-driven resume analysis and ATS scoring platform. Built 15+ REST API endpoints, integrating Google Gemini 2.5 Flash for resume analysis, AI-based resume rewriting, and interview-question generation using structured JSON prompts. Implemented complete job-tracker CRUD functionality with SlowAPI rate limiting and CORS handling, following clean and maintainable backend design practices. Dockerized the backend and deployed it on Render, validated using 15+ automated Pytest/HTTPX test cases and a GitHub Actions CI/CD pipeline for reliable builds.
Backend Developer
Sensor Fault Detection
Present - Present
Built a modular Flask backend and ML pipeline for wafer sensor fault detection, with dedicated modules for data ingestion, preprocessing, model training, and prediction, connected to MongoDB. Implemented custom logging and structured exception handling (logger.py, exception.py) across the pipeline for effective debugging and reliable execution tracking. Trained and compared 4 ML classification models (XGBoost, Random Forest, Gradient Boosting, SVC), using GridSearchCV for hyperparameter tuning and automated best-model selection. Built Flask routes for CSV upload, prediction, and result download, applying saved preprocessing artifacts to ensure consistent, leak-free inference.
Education
B.Tech — Computer Science Engineering (AI & ML) - Government Engineering College, Aurangabad, Bihar
2022 - 2026 · Afghanistan
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Relocation
Open to Relocation
Skills (39)
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