Hi, I'm Alok
Senior Quality Assurance Engineer
Senior Software QA Engineer with 6+ years of experience building reliable software through intelligent scalable automation, quality engineering, and AI-assisted development.
Quality engineering, thoughtfully practiced.
I'm a Senior QA Engineer based in Nepal, working with international teams to ship reliable software through scalable automation, leadership, and AI-assisted engineering.
I'm Alok Giri, a Senior Quality Assurance Engineer with over 6 years of experience designing scalable test automation, leading quality initiatives, and improving release confidence for web and mobile applications.
My focus is quality engineering at scale — combining Cypress with TypeScript, Selenium with Python, and modern AI-assisted workflows (Cursor, Claude Code, GitHub / Atlassian / Figma MCP) to accelerate automation, sharpen coverage, and elevate engineering productivity.
I've worked remotely with international teams, led QA initiatives across three companies, mentored engineers and currently teach Agile Software Development and Software Testing, Verification, Validation & QA as a part-time lecturer at Pokhara University — because I believe the best engineers keep learning and pass it on.
A journey of quality engineering.
From individual contributor to QA leadership — building automation, mentoring teams, and driving process improvements across three companies.
Key Contributions
- Leveraged AI-assisted engineering workflows (Cursor, Claude Code, MCP integrations) to accelerate automation development, test execution, reporting, and cross-team collaboration.
- Streamlined collaboration across development, QA, design, and project management using GitHub MCP, Atlassian MCP, and Figma MCP.
- Developed and maintained scalable automated test suites using Cypress and TypeScript, improving regression coverage and testing efficiency.
- Led QA efforts for web applications through combined manual and automation testing strategies.
- Designed and executed structured test plans, validation strategies, and comprehensive test cases across multiple application flows.
- Reviewed functional specifications, design documents, and business requirements to ensure complete test coverage and product quality.
- Performed functional, regression, smoke, integration, and end-to-end testing across development and staging environments.
- Identified, documented, tracked, and validated software defects using structured defect management workflows.
- Collaborated closely with developers, product managers, and cross-functional teams to validate fixes and ensure release quality.
- Mentored junior QA engineers and promoted QA best practices across teams.
- Improved software reliability and release confidence through continuous QA process enhancements.
A modern QA toolkit.
40 tools, disciplines and methodologies — spanning automation, AI-assisted engineering, and quality leadership.
Automation
4 skills
Scalable test automation frameworks for web & mobile.
Programming
5 skills
Languages I use to build tests and automation tooling.
AI-assisted Engineering
6 skills
Modern AI-powered workflows that accelerate QA development, execution, and reporting.
API Testing
2 skills
Contract validation, integration testing, and API workflows.
Performance
1 skill
Load and stress testing to validate scalability.
Testing Disciplines
13 skills
End-to-end coverage across manual and automated strategies.
Methodologies
4 skills
Agile delivery and structured software lifecycles.
Leadership
5 skills
Team enablement, strategy, and cross-functional collaboration.
Quality is engineering, not testing.
Six lenses into how I think about software quality — the principles, patterns, and processes that shape reliable software delivery.
My working principles.
Five beliefs that shape how I approach quality — from the first requirement conversation to the post-release retro.
Quality is everyone's responsibility
QA doesn't own quality alone — engineers, designers, product, and leadership all shape it. My job is to make quality visible and easy to act on.
Shift left, catch early
The cheapest defect is the one prevented in requirements. I invest in reviews, risk analysis, and early automation to shorten the feedback loop.
Automation increases confidence, not replaces critical thinking
Automation is a safety net for known risk. Humans still find the unknown — exploratory testing, edge cases, and UX judgment stay in the loop.
Collaboration is essential
Great QA happens in conversation — with developers, designers, PMs, and customers. Silos produce bugs; shared understanding produces quality.
QA enables fast, safe releases
The goal isn't to slow shipping. It's to ship faster with less fear. Every automated check I build buys the team more confidence per hour.
Build the safety net in the right shape.
A healthy test portfolio is fast at the base and thin at the top. Hover or tap a layer to explore what belongs there.
Fast, focused, developer-owned checks.
Low
Fast
Tools I use
Examples
- Function returns expected output for inputs
- Utility handles null/edge cases
- Component renders with props
Quality flows through every stage.
From code commit to production release — every step has a QA touchpoint that reduces risk and increases release confidence.
Step 1
Developer
Code is written, unit tests run locally.
Step 2
Pull Request
Changes reviewed by peers and QA.
Step 3
Automated Tests
CI runs unit, integration, and E2E suites.
Step 4
Manual Verification
Exploratory and UX validation for high-risk changes.
Step 5
Staging
Production-like environment for final checks.
Step 6
Production
Deployed to real users with monitoring.
Developer
Code is written, unit tests run locally.
Pull Request
Changes reviewed by peers and QA.
Automated Tests
CI runs unit, integration, and E2E suites.
Manual Verification
Exploratory and UX validation for high-risk changes.
Staging
Production-like environment for final checks.
Production
Deployed to real users with monitoring.
Automate the boring, explore the interesting.
Automation is the safety net. Manual testing is the insight. The best QA programs know exactly where each belongs.
Automate
Deterministic, repetitive, high-frequency.
Regression suites
Repetitive, high-volume validation.
Smoke tests
Catch broken builds in minutes.
API contract tests
Deterministic, fast, stable.
Data-driven scenarios
Same flow, many inputs.
CI gate checks
Block bad code before merge.
Cross-browser sanity
Matrix testing at scale.
Keep Manual
Judgment-heavy, exploratory, human-centric.
Exploratory testing
Uncover the unknown unknowns.
UX evaluation
Human judgment on feel and clarity.
Edge cases
Creative pathfinding beats scripts.
Brand-new features
Stabilize before automating.
Accessibility review
Assistive tech + human validation.
Visual regression review
Snapshot + human sanity check.
Automation gives us the safety net. Manual testing gives us the insight.
AI amplifies engineering — it doesn't replace judgment.
How I integrate Cursor, Claude Code, and MCP tools into a QA workflow that ships faster with better coverage.
Requirements Analysis
Feed specs, tickets, and design context to the AI to surface edge cases and testing angles before writing any tests.
AI-assisted Test Generation
Draft Cypress specs, data fixtures, and page objects from natural-language intent — refined with codebase context.
Human Review & Refinement
I edit for correctness, coverage, and maintainability. AI suggests; engineering judgment decides.
Execution & Debugging
Run suites in CI, triage failures with AI-assisted stack trace analysis, and get quick reproduction hints.
Reporting & Feedback
Summarize runs, link failures to tickets, and communicate risk to the team — faster than a manual write-up.
Faster
Test creation
Draft specs and fixtures from intent, not blank files.
Broader
Coverage
AI surfaces edge cases and negative paths I might miss.
Clearer
Communication
Auto-summaries and structured reports for cross-team alignment.
A continuous loop, not a straight line.
Every release teaches something. This is the eight-step cycle I run — and re-run — with each iteration sharpening the last.
Step 1 of 8
Requirements
Requirements
Read specs, understand the user, ask the questions everyone forgot to ask.
Continuously learning, continuously teaching.
Formal education, professional certifications, and a commitment to sharing knowledge with the next generation of engineers.
Masters of Computer Information System
Pokhara University · Nepal
Relevant Coursework
IBM Data Science Professional Certification
IBM
Hands-on skills in Data Science and Machine Learning using Python, SQL, cloud labs, and data visualization.