Public-safe builder profile

Quality engineer with a builder's brain and a researcher's sense of evidence.

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.

Pritom Kumar Mondal
Profile status Building public evidence
Public repos41
Top repo stars40
Research repo forks11
Languages4
Snapshot

What a recruiter should understand in 20 seconds.

01

Senior QA automation depth

Python-first SDET experience across web, mobile, API, CI/CD, dashboards, and high-risk transaction workflows.

02

AI tooling instincts

Practical AI work around review assistance, data querying, document intelligence, and quality analysis.

03

Research credibility

Published ML/SDN work with a public repository that has meaningful stars, forks, and reproducibility notes.

04

Product curiosity

Independent tools like AngleMate and LinkedApply show taste beyond test automation: user problems, UX, and shipping.

Experience

Professional work, described without leaking private project names.

Software Development Engineer in Test, Binance

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.

PythonPytestSeleniumAppiumAI toolingCI/CD

SDET, AiFi Inc.

Designed automation architecture and deployment validation for automated retail systems using Python, Kubernetes, Docker, Selenium, and Azure-backed workflows.

KubernetesDockerSeleniumAzure

MS Computer Science, Shanghai Jiao Tong University

Research work across machine learning, SDN, network traffic classification, NLP, and data-driven systems. Scholarship, top 5%.

MLSDNNLPResearch
Public projects

Open work that can be inspected.

Featured research repo

Network Traffic Classification

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.

40stars
11forks
300k+flows
99.29%reported result
Open repository
Research

Evidence, baselines, and reproducibility.

Dynamic network traffic classification

Supervised machine-learning classification for Docker-based SDN traffic, connected to a public repository and DOI-linked paper trail.

News popularity forecasting

BN-LSTM based forecasting work connecting NLP, ranking behavior, and predictive modeling.

Bioinformatics ML

PCA, regression, SVM, and neural-network experiments for disease prediction and classification workflows.

Live GitHub pulse

This section updates from public GitHub data.

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Interview mode

Strong answers, public-safe framing.

Answer

What makes you different?

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.

Quality systemsAutomationAI toolsResearch