AI QA Lead Engineer

Role Summary

Responsible for establishing quality standards, testing strategy, release readiness, and data validation practices across the platform.

Responsibilities

• Define test strategy and quality standards
• Lead integration, regression, UAT, and release testing
• Validate migration outcomes and data reconciliation results
• Drive automation strategy
• Mentor QA resources and establish best practices

Required Skills

• Test Automation
• API Testing
• SQL Validation
• Data Reconciliation Testing
• Integration Testing
• Regression Testing
• UAT Coordination
• Release Validation
• Defect Management
• Root Cause Analysis

GenAI

• Hands-on experience with GenAI coding assistants (Claude Code, GitHub Copilot, or similar) for AI-assisted development.
• Prompt engineering skills writing clear instructions and feedback to guide AI toward desired output.
• Ability to critically review and validate AI-generated code and content before use.
• Basic understanding of LLM concepts — RAG, context windows, hallucinations, and responsible AI practices.

Preferred Skills

• Databricks Validation Frameworks
• PySpark Testing
• Performance Testing

Banking Applications