Vineet Kumar, QE Manager and SDET Architect

Vineet Kumar

QE Manager, SDET Lead, Test Automation & AI-Driven Quality Engineering

Quality shouldn’t slow delivery. It should be the reason you ship faster. Five-day regressions now finish in four hours. Production defects are down 25%. Five AI agents do the triage nobody wants to. I lead 12 SDETs on a large financial services platform, and I still build the automation myself.

Explore My Impact View Resume

AI Agents Architecture 🤝 Open-source projects Case studies

contactkrvineet@gmail.com · 📍 Canada

Hands-on engineering skills

Leadership portfolio

Leading Quality Transformation

Quality transformation is more than adding tests. It brings strategy, engineering, delivery, and people together around better decisions and dependable releases.

Set the quality direction

Define a practical QE strategy, risk-based quality gates, release criteria, and metrics aligned to product and delivery goals.

Build scalable engineering

Modernize UI, API, performance, and CI/CD automation into maintainable feedback systems that teams can use throughout delivery.

Grow teams and new capabilities

Hire, coach, and align cross-functional teams while extending quality practices to AI model evaluation and emerging workflows.

How I approach quality

Practical principles

Assess before you automate

Start with a focused review of test strategy, automation, CI/CD and release risk, and end with a prioritized action list.

Automation & CI/CD roadmap

Plan in 90-day steps to cut regression time and flaky tests, with metrics to track progress.

AI-assisted QE

Pilot AI agents for test triage and review with human checks, and evaluate LLM features rigorously.

Team leadership

Hire, coach and structure SDET teams, and lead quality through critical deliveries.

Explore Case Studies Browse Engineering Portfolio

How I Architect Test Automation

Six layers, one rule: each layer has a single job. A commit flows down the stack and only the tests that matter run.

  1. ScenariosBDD, business language
  2. StepsThin, reusable glue
  3. Page objectsOne-file UI fixes
  4. Core servicesSelf-healing, LLM data
  5. CI/CDSmart test selection
  6. ReportingGated, parallel runs

5 days → 4 hours regression  ·  −25% production defects  ·  5 AI agents speeding script development

Explore the full architecture →

A connected QE operating model

How AI Agents Connect Across Quality Engineering

This representative workflow shows how focused agents can connect requirements, test design, code changes, and release evidence. People review agent output and retain ownership of merges and release decisions.

  1. Clarify the change

    Input Business requirements Confluence
    AI agent 1 Requirements to acceptance criteria Structures criteria for the matching work item
    Work item Jira acceptance criteria BA and QE review before test design

  2. Turn intent into reusable tests

    Reviewed input Jira acceptance criteria Expected behavior and examples
    AI agent 2 Feature file and step reuse Drafts Gherkin and finds existing steps
    SDET review Reusable Cucumber scenarios Engineer checks intent and coverage

  3. Build on shared automation assets

    UI context DOM and Page Objects Existing components and locators
    AI agent 3 Component and locator reuse Finds reusable assets or drafts additions
    SDET review Framework-aligned automation Validate locators before they enter the suite

  4. Analyze every proposed code change

    Code change GitLab merge request Diff and impacted modules
    AI agent 4 Code review Checks framework conventions and suggests fixes
    AI agent 5 Smart test impact Selects impacted tests for fast PR feedback

    Human control: Engineers assess review suggestions and own the merge decision.

  5. Prove readiness with layered evidence

    Fast feedback Targeted PR checks Relevant tests run first
    CI execution Jenkins or GitLab CI Full regression is the second-pass safety check
    Evidence and decision Allure and quality scorecard QE and engineering leaders retain release approval

Illustrative workflow based on the QE agents and delivery practices described in my portfolio. Enterprise implementation details are generalized.

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Live Tools & Sandboxes

Working AI agents and hands-on practice environments — not just slideware.

Live AI Agent

Code Review Agent

AI-assisted pull-request reviewer that flags bugs, smells, and risky changes.

Launch
Live AI Agent

Flaky Analyzer

Detects and triages flaky tests so teams can stabilize CI pipelines faster.

Launch
Showcase

Featured Projects

A curated walkthrough of production-grade automation frameworks and architecture.

Explore
Sandbox

Python Sandbox

In-browser environment to practice Python automation patterns.

Open
Sandbox

SQL Sandbox

Practice SQL queries used in data validation and test data setup.

Open

🎓 Free Test Automation Resources & Open Source Frameworks

Empowering the SDET community through knowledge sharing. When we share expertise, we multiply innovation and accelerate quality engineering excellence.

Discover production-ready automation frameworks and libraries designed to help QA engineers, SDETs, and DevOps teams master modern testing patterns including Selenium, Playwright, API testing, CI/CD integration, and performance testing strategies.

Explore Automation Frameworks Contribute / Collaborate

🚀 Test Automation Projects & Enterprise-Ready Frameworks

Explore production-grade automation frameworks for Selenium, Playwright, API testing, and performance engineering. Each framework is CI/CD-ready, scalable, and built with industry best practices.

Playwright With Behavior-Driven Development Framework Playwright BDD framework supporting multiple reporting options including Cucumber HTML, Allure, and Bootstrap

A comprehensive test automation framework combining Playwright with BDD (Cucumber) and multiple reporting options.

Features :

  • Dual Test Approach: Playwright or BDD Cucumber
  • Multiple Reports: Allure, Cucumber HTML, Playwright reports
  • Screenshot on Failure: Automatic screenshot capture for failed tests
  • Parallel Execution: Support for parallel test execution
  • Headed/Headless Mode: Configurable browser modes
  • Multi-Browser Support: Chrome, Firefox, Safari (WebKit)
  • CI/CD Ready: GitHub Actions included
Read More Live Report View Project

REST API Development Framework-Express Service Boilerplate A framework where anyone can spin up / bootstrap their own REST API quickly and is built with Node.js, Express, and MongoDB Atlas.

A RESTful API built with Node.js, Express, and MongoDB Atlas for managing user data. This project demonstrates modern API development practices with proper validation, error handling, and cloud deployment.

Features :

  • RESTful API Design: Follows REST principles for resource management
  • CRUD Operations: Create, Read, Update operations for user data
  • Input Validation: Schema validation using Zod
  • Error Handling : Centralized error handling middleware
  • MongoDB Integration: Cloud database with Mongoose ODM
  • Environment Configuration: Secure secret management
  • CORS Support : Cross-origin requests enabled
  • CI/CD Pipeline : Automated deployment with GitHub Actions
  • Production Ready : Optimized for serverless environments
Read More View Project

Python BDD Test Automation Framework A robust, scalable test automation framework built with Python, Behave (BDD), Selenium, and Requests for comprehensive UI and API testing with beautiful HTML reports.

This framework offers a comprehensive solution for behavior-driven testing of web applications and RESTful APIs. Designed with clean architecture principles and following BDD best practices, it enables teams to write human-readable test scenarios that serve as living documentation

Features :

  • BDD with Behave: Write tests in Gherkin syntax (Given/When/Then) for better collaboration between technical and non-technical team members
  • Dual Testing Support: UI automation with Selenium WebDriver and API testing with Requests library
  • Multiple Report Formats: modern, interactive HTML dashboards - Allure and Behave
  • Page Object Model : Clean separation of test logic and page elements
  • Parallel Execution: Run tests in parallel for faster feedback
  • Environment Configuration: Runs in QA, Dev and Staging; read-only smoke checks on Production
  • CI/CD Pipeline : Easily integrate with Jenkins, GitHub Actions, GitLab CI, etc.
  • Logging & Screenshots : Automatic capture on test failures
Read More Live Report View Project

AI-Enabled Test Automation Framework A robust, scalable test automation framework built to leverage AI for smarter testing

This AI-enabled, behavior-driven test automation framework delivers end-to-end coverage for web applications and RESTful APIs — with self healing capabilities and test data generation using LLM models . Grounded in clean architecture and BDD best practices, it harnesses large language models to author, heal, and analyze tests, transforming human-readable scenarios into self-maintaining living documentation

  • Self Healing Locators: When a selector fails LLM Model analyzes the failure
  • AI Data Generation: AI generates valid, boundary, and invalid data sets per field type — eliminating hand-crafted data tables
  • Multiple Report Formats: modern, interactive HTML dashboards - Allure and Behave
  • Page Object Model : Clean separation of test logic and page elements
  • Parallel Execution: Run tests in parallel for faster feedback
  • Environment Configuration: Runs in QA, Dev and Staging; read-only smoke checks on Production
  • CI/CD Pipeline : Easily integrate with Jenkins, GitHub Actions, GitLab CI, etc.
  • Logging & Screenshots : Automatic capture on test failures
View Project

Gatling Performance Framework

A Gatling-based performance framework (Java) to create repeatable, CI-friendly load tests for APIs and web endpoints. Designed for realistic user scenarios, configurable test data, pass/fail assertions and rich HTML reporting.

Quick start: add the Gatling Maven plugin, place simulations in src/gatling/simulations, provide feeders in src/gatling/resources, then run mvn gatling:test.

Technology Used : Java , Maven , BDD , TestNG , Gatling

RestAssured Framework

This framework is created to simplify the process to test API Use Case by using Rest Assured Libraries.

This framework can be utilized by cloning the repo URL and changing the endpoints and payload of the services under test.

Technology Used : Java , Rest Assured , Maven ,Cucumber

UI Automation Framework

A page-object-driven Selenium framework for browser UI test cases, built for maintainability and parallel execution.

Technology Used : Java , Selenium , BDD , Maven

Mobile Automation Framework (Android)

An Appium-based mobile automation framework for testing native and hybrid Android apps.

Technology Used : Appium , Java , Selenium , Android