A developer with strong self-learning abilities, analytical thinking and problem-solving skills, highly motivated, professionally curious, and driven by a constant desire to learn, grow, and stay up to date.
Ground - Cloud-based platform for agricultural data management and analytics

Mentored by: Vast Data
Mentors:
Data Science Bootcamp 2025 (Data)
Responsibilities:
Schemas & Validation: Built JSON/Protobuf schemas for sensor readings with validation scripts, folder-level checks, and CI integration (GitHub Actions + Spark cleaning job).
Graph-Based Background Removal: Implemented classical segmentation for foreground masks and cut-outs, with CLI/API wrapper and post-processing.
Disease Anomaly & Worsening Detection: Developed offline pipeline for detecting anomalies and worsening trends in disease data using statistical baselines, Z-score/IQR/CUSUM, alert deduplication, and Postgres logging.
Soil Moisture Detection Pipeline: Built real-time CV pipeline (FastAPI + MobileNetV3) for wet/dry classification with threshold-based alerts, Kafka messaging, DLQ retries, and Postgres event logging.
End-to-End Integration: Connected full pipeline from MinIO image ingestion → Kafka → Flink processing → inference service → DB updates → PyQt GUI visualization of zones, history, and irrigation status.
PyQt GUI: Created interactive map showing all sprinklers, active zones, last images, history, and parameter updates

Course Management System | MERN Stack
Developed a SaaS platform for managing courses with interfaces for admins, teachers, and students, supporting attendance, payments, and dynamic forms. The responsive front-end built with React and Redux Toolkit handles complex state, while the back-end with Node.js and Express provides secure REST APIs with JWT authentication and role-based access control. Data is efficiently modeled in MongoDB for scalability and fast retrieval.
Working Proficiency