Senior SDET and QA automation depth
Python-first SDET experience across Web3, blockchain, web, mobile, API, CI/CD, dashboards, and high-risk transaction workflows.
Pritom Kumar Mondal · SDET · AI QA Engineer · Web3
AI-powered QA and Web3 test automation, built with a researcher's sense of evidence.
I am Pritom Kumar Mondal, a Software Development Engineer in Test (SDET) and AI QA engineer specializing in Web3 and blockchain quality, Python automation, Selenium, Appium, API testing, CI/CD, and research-backed engineering tools.
Profile status
Building public evidence
Public repos41
Top repo stars40
Research repo forks11
Visits
5000+
Snapshot
What a recruiter should understand in 20 seconds.
AI QA and engineering tools
Practical AI engineering around review assistance, data querying, document intelligence, and software quality analysis.
Research credibility
Published ML/SDN work with a public repository that has meaningful stars, forks, and reproducibility notes.
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 Web3, blockchain, and high-risk financial technology systems: web/mobile/API validation, CI/CD quality gates, AI-assisted review flows, dashboards, alerting, and data-driven QA analysis.
Web3 QABlockchain testingPythonPytestSeleniumAppiumAI 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.
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
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.
Read the publicationNews 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.
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