Converted high-risk manual test cases into stable automated regression and functional tests, significantly reducing post-release defects

Currently implementing Robot Framework with Python for keyword-driven automation across web and desktop applications, improving maintainability and enabling non-technical contributors

Integrated automated testing into CI/CD pipelines using Jenkins, GitLab CI/CD, and Azure DevOps, executing tests on every commit with JUnit reporting and quality gates via SonarQube

Leveraged AI-assisted testing techniques using Playwright MCP and GitHub Copilot to accelerate test creation and reduce maintenance when UI elements change

Developed and executed API automation tests using Postman collections integrated into CI/CD, validating backend services and database state prior to release

Authored and maintained test plans, test cases, automation documentation, and execution reports, ensuring traceability from requirements through validation

Partnered closely with Product Managers and Engineers to incorporate testing considerations early in the development lifecycle

Led internal workshops to standardize automation best practices and improve test quality across teams

Migrated test management from Excel to Azure Test Plans, improving visibility, execution tracking, and audit readiness

Automated diagnostics and validation workflows using Python and Netmiko, reducing manual triage time and enabling early issue detection

Built automation using UiPath and Power Automate for monitoring, alerting, and automated ServiceNow ticket creation

Perform backend verification and log-based validation of batch processing workflows (Control-M, SQL Server, legacy financial systems) to ensure data integrity and operational correctness

Conduct root cause analysis and collaborate with cross-functional teams to resolve incidents impacting SLAs

Regularly validate system health across Windows and Linux environments, supporting reliability and operational compliance

Contributed to automated test case development to support application verification prior to release

Collaborated with QA and Product teams to identify test coverage gaps and improve release confidence

Created isolated sandbox and test environments to support verification activities and reduce deployment risk

Supported verification & validation (V&V) activities for learning management platforms through structured UAT, test execution, and defect tracking

Containerized applications using Docker and evaluated Kubernetes for scalable test environments

Automated system integrations and deployment validation workflows using Python

Developed PowerShell automation scripts for server health checks, patch validation, and connectivity testing

Supported operational reliability through proactive system monitoring and validation