writick = {
    "role"        : "Backend & Data Pipeline Engineer",
    "degree"      : "M.E. CSE @ TIET Patiala (CGPA: 9.72 / 10 — Top of Batch)",
    "gate"        : "GATE 2025 Qualified — CS & IT",
    "internship"  : "TCS iON Kolkata — Production NLP Backend (125hr delivery)",
    "core"        : ["System Design", "REST APIs", "Data Pipelines", "ML Backends"],
    "strengths"   : ["DSA (C++)", "OS", "DBMS", "Computer Networks", "OOP"],
    "cloud"       : "AWS Academy Cloud Foundations Trained · Credly Verified",
    "currently"   : "Seeking SDE / Backend / AI Eng Internship Opportunities"
}

I don't just write code. I think about why the system works — and more importantly, why it breaks.

Python · Flask · SQLite · Docker · Scikit-learn · REST API

Real-world backend system for multi-node log ingestion and real-time anomaly detection.

Python · Pandas · NumPy · KD-Tree · Power BI

Data engineering pipeline for detecting orbital collision risks across satellite constellations.

• KD-Tree spatial indexing — fast proximity search on massive orbital datasets
• ~30% faster preprocessing via vectorized operations over naive loop approaches
• Batch risk-scoring workflows that rank satellite encounters by collision probability
• Power BI dashboards visualizing collision clusters and temporal risk trends

PHP (Laravel) · MySQL · C++ · Firebase · Google Maps API

Full-stack real-time telemetry system — from sensor data to live visualization.

• C++ STL modules for sensor preprocessing and driving-pattern classification
• Laravel backend with concurrent REST API handling; trip data persisted in MySQL
• Firebase real-time location streaming + Google Maps live visualization
• Relational schema design + batch analytics pipelines for driver behavior insights

Python · XGBoost · Scikit-learn · SHAP · Streamlit

ML pipeline for heart disease prediction with interpretability at its core.

• Trained on 250K+ records; SHAP-based feature-level explanations for every prediction
• Deployed via Streamlit for interactive clinical risk assessment

Python · Flask · Pandas · Linux · Git

Built a production-quality NLP backend for grammatical error detection.

• Delivered a complete working prototype within a 125-hour constraint — on time, to enterprise standards
• Designed REST data workflows with regression testing and automated evaluation loops
• Pandas ETL pipelines transforming raw unstructured text into production-ready datasets
• Structured logging, input validation, and exception handling baked in from day one

✅  GATE 2025 Qualified — Computer Science & Information Technology
✅  Top of Batch — M.E. CSE 1st Semester, TIET (CGPA 9.72 / 10)
✅  Production backend delivered in 125 hours @ TCS iON
✅  150+ DSA problems solved (LeetCode · GeeksForGeeks)
✅  AWS Academy Graduate — Cloud Foundations Training Badge (20hrs) · May 2026
✅  AWS Cloud Assessment Certification — LearnTube.ai
✅  Machine Learning with Python — Globsyn Business School

Actively seeking SDE / Backend / AI Engineering internship opportunities — on-campus or off-campus. Response time: usually within 24 hours.

"Build systems that survive production. Write code that survives review."