AI Quality Engineer QA

About the job

Summary:

The AI QA Engineer will be responsible for ensuring quality, reliability, and performance of AI‑driven applications and enterprise systems. The role requires strong proficiency in modern automation frameworks, hands‑on experience validating AI/ML workflows, solid understanding of AWS cloud environments, and deep domain knowledge in Retail Banking and Consumer Finance. The candidate must be able to design and execute Regression Packs, System Testing (ST), and System Integration Testing (SIT) for highly complex modules and enterprise financial applications.

Required Experience & Skills

• 5–10+ years of experience in Quality Engineering, with strong exposure to automation testing for enterprise applications.
• Expertise in latest automation tools for UI, API, and integration testing.
• Experience validating AI/ML solutions, including model output testing and bias/anomaly monitoring.
• Strong experience with AWS cloud for test execution, environment validation, and automation.
• Deep domain knowledge in Retail Banking and Consumer Finance, including lending, cards, payments, fraud, and risk workflows.
• Proven ability to design and execute Regression Packs, ST, and SIT for large-scale, complex systems.
• Solid understanding of microservices, APIs, event-driven systems, and data pipelines.
• Good Experience and understanding of Defect Management tools like Jira etc
• Excellent analytical, problem‑solving, and documentation skills.

Roles & Responsibilities

Key Responsibilities

• Design, develop, and execute automation test suites using the latest frameworks  - any two (Selenium, Cypress, Playwright, RestAssured, TestNG, JUnit, PyTest, Karate, etc.).
• Validate AI/ML model outputs, integrations, and functional behavior across end-to-end workflows.
• Build and maintain comprehensive regression packs for critical banking and consumer finance applications.
• Lead ST and SIT cycles for complex, integrated enterprise modules, ensuring coverage of functional, integration, API, performance, and security scenarios.
• Work with cross‑functional teams to analyze business requirements and translate them into detailed test scenarios and acceptance criteria.
• Use AWS tools/services (CloudWatch, S3, Lambda, API Gateway, SageMaker validation flows) for cloud-native quality assurance.
• Automate data validation, model validation, and workflow testing for AI-led features.
• Identify defects early, drive root‑cause analysis, and ensure continuous quality improvements