MAS-Quality Engineering-Supervisor at RSM

Setup: Hybrid
Location: Hyderabad, Telangana
Type: Full-time
Level: not_specified
Posted: Aug 4, 2026

Description

The Supervisor - ERP Quality Engineering (Manual & Automated Testing) will plan, design, execute, and automate testing activities for large-scale ERP and digital transformation programs. This role involves performing end-to-end testing for various ERP platforms, developing and maintaining automated test suites, utilizing test management tools, and ensuring high-quality software delivery.

Required Skills

Microsoft Dynamics 365
NetSuite
Oracle
SAP
Salesforce
Leapwork
Playwright
Azure DevOps
Jira

Additional Information

Required Previous Experiences: 8+ years of experience in Quality Assurance and Testing.
Strong experience in both manual testing and automation testing.
Hands-on experience with ERP platforms such as Dynamics 365, SAP, Oracle, NetSuite, or Salesforce.
Experience creating detailed test scenarios and scripts from business requirements.
Experience with test management and defect tracking tools.
Understanding of SDLC, Agile, and DevOps.
Preferred Qualifications: Experience with ERP business process testing (O2C, P2P, R2R, L2C).
Experience with API testing and integration testing.
Exposure to performance testing concepts and tools.
Experience leveraging AI-assisted testing tools and techniques to improve test design, test data generation, test automation development, defect analysis, and overall testing efficiency.
Familiarity with generative AI platforms such as GitHub Copilot, Microsoft Copilot, ChatGPT, or similar tools for accelerating test case creation, automation script development, and quality engineering activities.
Exposure to testing AI-enabled applications and generative AI solutions, including validation of AI outputs for accuracy, consistency, reliability, security, and responsible AI principles.
Familiarity with common AI quality risks such as hallucinations, prompt sensitivity, non-deterministic responses, bias, data grounding issues, explainability, and model drift is preferred.