Raman Sharma

About Raman Sharma

Expertise in requirement analysis, test strategy/approach, test effort estimation, test planning, execution, metrics, reporting, defect management, data, environment setup- Proficient in providing value-added, initiatives, to the client to reduce efforts & cost, increase quality, reduction in production defects, increased productivity/efficiency- Proficient in building PoCs & experience in pre-sales creating RFP, RFQ, proposal- Experience in getting new business from the client by showcasing PoC, solutions to customer pain areas.Automation Framework & DevOps Skills:Big Fan of Gangs of Four - Design PatternsData Driven, Key Word, Hybrid, Page Object Model, Selenium WebDriver/GRID, AutoIT, SIKULI, Groovy, TDD (JUNIT/TestNg), BDD (Cucumber-JVM, JBehave), Serenity, SVN/GIT, MAVEN/ANT, Jenkins, SauceLabs, BrowserStack, Docker, AWSAI Tools: AppliToolsMobile Test Automation:iOS & Android devices: Native/Hybrid/Browser apps testing using Appium, BrowserMobProxy, Charles ProxyAPI (Backend) WebServices Automation:SOAP, REST, HttpClient, Gson, SimpleJson, Jackson, Jersey Client, REST Assured, SOAP UI, JSON, XML, JMETER, Swagger, Marshelling, Un-Marshelling, Mockito, WireMock, POSTMAN, NEWMAN, CURLPerformance Testing: Jmeter, LocustDev Ops: Maven, Jenkins, Docker, ZaleniumCode Coverage: JaCoCo, Cobertura, SonarQubeProject/Test management Tools: JIRA, Rally, HP-QC/ALM, Test RailDB: Oracle, MySql, MongoDB Cloud Technologies:VmWare, AWS Certification : ISTQB foundation levelGithub Profile- https://github.com/raman261

Experience

1. Test Automation Architect

   Air India Limited

   Aug 2024 — Present

   Leading Quality Engineering transformation for Air India’s mission-critical digital platforms (MyAI-Web, DG, AISmart), with a focus on automation at scale, AI-driven testing, performance engineering, and cost optimization.I own the end-to-end QA strategy across UI, API, integration, performance, and AI validation, partnering closely with Product, Engineering, DevOps, and Leadership to ensure predictable releases, high availability, and controlled risk.Key contributions include:Re-architected test strategy, shifting deep validations from brittle E2E tests to integration and contract testing, validating application logic, data transformation, and database persistence while reducing dependency on unstable subsystem environments.Built enterprise-grade automation frameworks (Playwright, REST Assured) and integrated them into CI/CD pipelines enabling daily and nightly unattended regression execution.Led performance engineering strategy, including workload modeling, capacity planning, automated performance testing, and release.Designed and executed a 6-month AI-driven QA transformation program, achieving 95% AI adoption, 70% automation coverage, and ~63% reduction in manual test maintenance effort.Built AI Test Case Generator and evaluation frameworks, reducing regression execution time from 2 days to 2 hours (96%).Eliminated Azure agent and vendor dependencies by building in-house QA infrastructure using Jenkins, Docker, and self-hosted ReportPortal, delivering ₹300K+ annual cost savings.Designed and developed a custom QA skill assessment platform to objectively evaluate automation, performance testing, and AI prompt-engineering capabilities across teams.Established quality gates, governance models, and risk-based validation, achieving 99% uptime and zero critical production defects.Focused on building scalable QA systems, not just test suites, and positioning Quality Engineering as a strategic enabler of speed, reliability, and innovation.

Education

• Rajiv Gandhi Prodyogiki Vishwavidyalaya

  Bachelor of Engineering (B.E.), Computer Science