Tech Stack

Description

In this client-facing role, you will lead testing activities including test planning, automation, and defect management for Azure-focused projects. You'll drive high-quality delivery by performing manual and automated testing, mentoring junior team members, and contributing to continuous improvement in quality engineering practices.

Requirements

• Bachelor’s degree in computer science or a closely related discipline
• Extensive experience in QA methodologies, test planning, automation, and quality engineering practices
• Strong proficiency in automation tools such as Selenium, Playwright, or equivalent frameworks
• Solid programming skills in Python, C#, Java, or similar languages
• Hands-on experience testing data pipelines, ETL workflows, and analytics platforms (Databricks, Fabric preferred)
• Understanding of machine learning principles, model evaluation metrics and AI system behavior
• Ability to test LLM behavior, prompts, and AI model outputs
• Ability to execute comprehensive test plans for AI models and systems
• Excellent analytical, problem-solving, communication, and presentation skills
• Demonstrated ability to work cross-functionally and influence engineering teams
• Passion for innovation, continuous learning, and staying current with emerging QE and AI technologies

Responsibilities

• Apply knowledge of Quality Engineering and testing areas (Automation, Performance, Data quality, AI, White box, Blackbox) to define and advise in test planning
• Drive high-quality delivery by performing both manual and automated testing, ensuring full coverage across functional, system, data, and AI components
• Lead the design and implementation of comprehensive test strategies, quality processes, and documentation for assigned project
• Maintain accurate record results, test cases, and share feedback to the project team
• Ensure all QE deliverables meet quality standards, aligning with project objectives and organizational best practices
• Lead defect triage process
• Develop and validate test data, data pipelines, and infrastructure from both functional and system perspectives
• Evaluate and test machine learning models, including LLM behavior, prompt robustness, model outputs, and performance metrics
• Mentor junior team members, promote QE best practices, and contribute to continuous improvement initiatives across the Quality engineering practice