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\n

\n \"Aayush\n

\n

\n \"Testing\n

\n

\n \"Portfolio\"\n \"LinkedIn\"\n \"Email\"\n
\n \"Medium\"\n \"X\"\n \"ORCID\"\n

\n

\n Open to Senior SDET, QA Automation Lead and AI Quality Engineering roles.
\n Gurugram · Bangalore · Hyderabad · remote-friendly\n

\n
\n

$ cat aayush.spec.yml

\n
role:        Senior SDET & Automation Architect @ Keywords Studios\nexperience:  4+ years, leading QA across multiple products at once\ndomains:     [gaming, banking, healthcare, e-commerce, logistics]\neducation:   MSc Data Science, BSc Computer Science\n\nwhat_i_do:\n  - architect test automation frameworks for web, API and mobile\n  - wire quality gates into CI/CD so regressions never reach main\n  - test LLM apps, RAG pipelines and AI agents like any other critical system\n  - run QA as a lead: test strategy, plans, reporting and release sign-off\n\nbuilding_now:\n  - one framework that runs UI, API and mobile suites from a single pipeline\n  - a microservices test automation framework\n  - LLM evaluation suites that fail the build when answers drift\n\nask_me_about: [Playwright, Selenium, RestAssured, Postman/Newman, DeepEval, Playwright MCP]
\n
\n

🧪 Featured work

\n

\n \"AI-Agent-Test-Using-Amazon-Nova-Act\"\n \"FullStack-E-Commerce-Testing\"\n \"API-Automation-Framework-Playwright\"\n \"Playwright-With-Cucumber-Using-JS\"\n \"AutoCart-Engine-FW\"\n \"Phoenix-Inwarranty-Flow-API-Tests\"\n

\n
\n

🧰 Automation Arsenal

\n

\n \"Automation\n

\n

Hands-on guides I built for the tools I use every day. Each one has runnable examples and an interactive simulator or playground, not just notes.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
GuideWhat's inside
Amazon Nova ActHow AI browser agents think, an agent playground, and how I built my Nova Act test framework
PlaywrightThe commands you'll actually use, an honest comparison with other tools, and a run-it-now demo
Selenium WebDriverHow WebDriver works under the hood, Page Object Model, and a browser automation simulator
TestNG + Java SeleniumAnnotations decoded, how the stack fits together, and an interactive test runner
GitHub Actions CI/CDA six-stage pipeline, trigger types, and a pipeline builder wired to real repos
\n

Coming next: Postman, Browser Use AI and Kane AI.

\n
\n

🧠 AI quality engineering

\n

AI features don't fail like normal code. The same prompt can pass today and hallucinate tomorrow, so I treat model output, retrieval quality and agent behaviour as things to measure and gate, not eyeball. My Amazon Nova Act agent framework runs end-to-end UI scenarios written in plain English.

\n
\n
\n
\n 
flowchart LR\n    subgraph AGENTS[\"AI-assisted automation\"]\n        A[\"Test intent<br/>in plain English\"] --> B[\"Playwright MCP<br/>planner · generator · healer\"]\n        B --> C[\"Playwright suites<br/>UI + API\"]\n    end\n    subgraph EVALS[\"Testing the AI itself\"]\n        D[\"LLM / RAG app\"] --> E[\"DeepEval<br/>pytest-style LLM evals\"]\n        D --> F[\"Ragas<br/>retrieval metrics\"]\n        D --> G[\"Promptfoo<br/>red-teaming\"]\n    end\n    C --> H{\"CI quality gate<br/>GitHub Actions / Jenkins\"}\n    E --> H\n    F --> H\n    G --> H\n    H -- pass --> I[\"🚀 Ship\"]\n    H -- fail --> J[\"🔍 Trace, report, fix\"]\n
\n
\n
\n \n \n Loading\n\n \n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
LayerToolsWhat it checks
Agentic UI testingPlaywright MCP, Playwright Test Agents, Amazon Nova Act, Browser-UseAgents explore the live app, draft test plans, generate specs and repair broken locators
LLM output evalsDeepEval (G-Eval, hallucination, answer relevancy)Pytest-style assertions on model responses, with pass/fail thresholds in CI
RAG evaluationRagasFaithfulness, context precision and context recall, so you know whether retrieval or generation is the weak link
Red-teaming & prompt regressionPromptfooPrompt injection, jailbreaks and PII leaks, plus side-by-side comparison of prompt versions
MCP servers & agentsMCP Inspector, tool-call assertionsTool schemas, tool-call accuracy and whether the agent actually reached its goal
ObservabilityLangfuse, Arize PhoenixProduction traces that feed real failures back into the eval dataset
\n

\n \"MCP\"\n \"DeepEval\"\n \"Ragas\"\n \"Promptfoo\"\n \"Amazon\n \"LangChain\"\n \"Claude\"\n \"GitHub\n \"Hugging\n

\n
\n

🛡️ Testing across the stack

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
AreaWhat I coverTools
UI & end-to-endPage Object Model, cross-browser runs, parallel execution, retries, screenshots and traces on failurePlaywright, Selenium
API & microservicesSchema validation, auth flows, chained requests, data-driven suites, environment configsRestAssured, Playwright API, Postman + Newman
MobileNative and hybrid app flowsAppium
BDDBusiness-readable scenarios that product owners can reviewCucumber, TestNG
PerformanceLoad and stress tests with pass/fail thresholdsJMeter, k6
SecurityOWASP Top 10 checks, HTTP security-header audits, reproducible bug reportsBrowser DevTools, Postman
CI/CD & infraPipelines that gate merges, containerised test runs, cloud test infrastructureGitHub Actions, Jenkins, Docker, AWS, Azure
Reporting & managementAllure dashboards, traceable test cases, plans and release reportsAllure, JIRA, Zephyr, TestRail
ProcessShift-left testing, IEEE 829-style test plans, QA leadership across parallel products
\n

\n \"Playwright\"\n \"Selenium\"\n \"Appium\"\n \"RestAssured\"\n \"Postman\"\n \"Cucumber\"\n \"TestNG\"\n \"JMeter\"\n \"k6\"\n \"Allure\"\n \"Jira\"\n \"TestRail\"\n

\n

\n \"Languages
\n \"DevOps
\n \"ML\n

\n
\n

📊 Signal

\n

\n \"GitHub\n \"Top\n

\n

\n \"Contribution\n

\n

\n \"Contribution\n

\n

\n \n \n \n \"Snake\n \n

\n
\n

\n Happy to talk test architecture, AI evaluation and quality engineering.
\n Explore my tool guides in the Automation Arsenal.
\n The fastest way to reach me is email or LinkedIn.\n

\n

\n \"Profile\n

\n

\"\"

\n
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