The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Enterprise SaaS QA Automation: AI-Powered Testing & CI/CD
My role : Senior QA Automation Engineer
Project description:
Architected and implemented comprehensive test automation framework for enterprise SaaS platform, achieving 92% API and 85% UI test coverage. Developed 230+ automated tests using Pytest and Selenium with Page Object Model design pattern. Integrated AI-powered test prioritization and flaky test detection achieving 95% accuracy. Implemented GitHub Actions CI/CD pipeline reducing regression testing from 2 days to 6 hours (75% reduction). Enabled daily releases while improving production defect detection by 82%. Technologies: Python, Pytest, Selenium WebDriver, GitHub Actions, Docker, Scikit-learn
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Polar — Local AI Desktop Assistant
Polar is a local, privacy-focused AI desktop assistant designed around a futuristic HUD interface and system-level interaction.
The project explored how a desktop AI could understand the user's environment, process visual and voice input, and respond or perform actions without relying entirely on cloud services.
Key features:
Local AI assistant architecture
Futuristic Tauri-based desktop HUD
Screen and contextual awareness
OCR-based extraction of text from the screen
Voice interaction pipeline
AI-powered context processing and responses
System-level desktop interaction and automation
Local/offline model execution
Real-time assistant-style interface
OCR pipeline:
Screen capture → OCR → Context extraction → Local AI → Response/Action → HUD
The project combined AI, computer vision, OCR, voice interaction, desktop application development, and modern UI engineering into a single experimental personal-assistant platform.
My contribution: Architecture, application development, AI integration, OCR functionality, UI/HUD development, and system interaction.
This is beautiful
Just wrapped up testing a multi-tenant SaaS build: signup, invites, company switching, role permissions. Found the kind of bugs that hide behind a working screen, like a table showing record numbers from another company's account instead of starting at 1.
If your app is close to launch and needs a second pair of eyes before real users find the bugs, happy to help.
Great example of testing beyond the happy path.
𝐘𝐨𝐮𝐫 𝐀𝐏𝐈 𝐢𝐬 𝐬𝐥𝐨𝐰. 𝐖𝐡𝐚𝐭 𝐝𝐨 𝐲𝐨𝐮 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐟𝐢𝐫𝐬𝐭?
When API response times start climbing, the first instinct is often to add more resources, introduce caching, or start rewriting code.
But a slow API doesn’t always mean a slow application.
The bottleneck could be hiding almost anywhere:
→ 𝐀𝐧 𝐮𝐧𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐪𝐮𝐞𝐫𝐲
→ 𝐀 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐢𝐧𝐝𝐞𝐱 𝐨𝐫 𝐍+𝟏 𝐩𝐫𝐨𝐛𝐥𝐞𝐦
→ 𝐁𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐈/𝐎 𝐢𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐞 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧
→ 𝐇𝐞𝐚𝐯𝐲 𝐬𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧
→ 𝐀 𝐬𝐥𝐨𝐰 𝐭𝐡𝐢𝐫𝐝-𝐩𝐚𝐫𝐭𝐲 𝐬𝐞𝐫𝐯𝐢𝐜𝐞
→ 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 𝐨𝐫 𝐭𝐢𝐦𝐞𝐨𝐮𝐭𝐬
→ 𝐄𝐱𝐡𝐚𝐮𝐬𝐭𝐞𝐝 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐩𝐨𝐨𝐥𝐬
→ 𝐂𝐏𝐔 𝐨𝐫 𝐦𝐞𝐦𝐨𝐫𝐲 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞
And sometimes adding Redis, more workers, or additional infrastructure only hides the real problem instead of solving it.
That’s why I prefer a simple principle: 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐟𝐢𝐫𝐬𝐭. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐬𝐞𝐜𝐨𝐧𝐝.
In production backend engineering, performance optimization should be driven by evidence, not assumptions.
I’ve summarized the areas I usually think about when investigating API latency in the post below.
