Senior QA automation depth
Python-first SDET experience across web, mobile, API, CI/CD, dashboards, and high-risk transaction workflows.
Public-safe builder profile
I build automation systems, AI-assisted engineering tools, and research-backed software projects. My public portfolio focuses on what can be shown openly: GitHub work, research artifacts, product experiments, and the engineering patterns behind my professional experience.
Python-first SDET experience across web, mobile, API, CI/CD, dashboards, and high-risk transaction workflows.
Practical AI work around review assistance, data querying, document intelligence, and quality analysis.
Published ML/SDN work with a public repository that has meaningful stars, forks, and reproducibility notes.
Independent tools like AngleMate and LinkedApply show taste beyond test automation: user problems, UX, and shipping.
Built automation and quality infrastructure for high-risk financial technology systems: web/mobile/API validation, CI/CD quality gates, AI-assisted review flows, dashboards, alerting, and data-driven QA analysis.
Designed automation architecture and deployment validation for automated retail systems using Python, Kubernetes, Docker, Selenium, and Azure-backed workflows.
Research work across machine learning, SDN, network traffic classification, NLP, and data-driven systems. Scholarship, top 5%.
Machine-learning experiments for classifying network traffic flows collected from a Docker-based SDN lab network. The public repository includes research artifacts, models, confusion matrices, reproducibility notes, and impact data.
Supervised machine-learning classification for Docker-based SDN traffic, connected to a public repository and DOI-linked paper trail.
BN-LSTM based forecasting work connecting NLP, ranking behavior, and predictive modeling.
PCA, regression, SVM, and neural-network experiments for disease prediction and classification workflows.
I am a QA engineer who thinks like a platform builder. I do not stop at writing automated checks; I care about evidence, dashboards, review loops, CI/CD quality gates, and systems that make engineering risk visible.