I engineer Python-based backend systems and the infrastructure around them. Focused on robust APIs in FastAPI, intelligent CI/CD automation, and containerized deployment workflows.
Final-year Computer Science undergraduate deeply focused on backend development, DevOps, and CI/CD automation. My core experience revolves around building reliable, Python-based backend systems with FastAPI.
I build systems designed for reliability and scale—from architecting containerized applications and relational databases with PostgreSQL/SQLAlchemy to orchestrating automated deployment pipelines and cloud infrastructure.
Whether it's writing strict automated testing suites or auditing server performance, I prioritize data integrity, reproducible deployments, and robust observability.
FlyRank AI | Chicago, US (Remote)
National Aerospace Science and Technology Park (NASTP) Institute of Technology | Lahore, PK
Focused on solving specific monitoring, automation, and data challenges.
Engineered a command-line infrastructure monitoring utility in Python to track HTTP/HTTPS endpoint availability and response latency. Utilized httpx for reliable network requests, persistent data logging for uptime reports, and Rich for an interactive terminal UI. Features strict URL validation and comprehensive automated test coverage via pytest, developed as the capstone architecture for Harvard's CS50P.
Architected a secure, containerized RESTful API for financial expense management. Engineered with FastAPI and a PostgreSQL database, utilizing SQLAlchemy ORM for complex data relations, and Alembic for automated schema migrations. Implemented stateless JWT Bearer authentication, validated endpoints with a comprehensive Pytest suite, and orchestrated the multi-service architecture using Docker Compose.
Architected a machine learning classification pipeline to detect SMS spam, resolving severe class imbalance using SMOTE synthetic oversampling. To prevent data leakage, synthetic generation was strictly isolated to training partitions across 108 experimental configurations. Evaluated multiple algorithms, with a TensorFlow-based Multi-Layer Perceptron (MLP) achieving the optimal balance of high accuracy and spam recall.
In development: Architecting an AI-powered DevOps observability tool to predict CI/CD pipeline failures and automate root-cause analysis. Integrating GitHub Actions and GitLab CI APIs with a scikit-learn machine learning engine to calculate real-time failure probabilities and reduce cloud compute waste. Engineering an LLM-based diagnostic layer to parse raw execution logs into actionable fixes, deploying the entire microservices architecture via Docker, Kubernetes, and Helm, with real-time telemetry exposed through Prometheus and Grafana.
Beaconhouse National University | Lahore
Currently open to backend roles, DevOps opportunities, and interesting technical challenges.