Senior Test Automation Engineer

Senior Test Automation Engineer

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

Role: Senior Test Automation Engineer(QA Tester/Computer Vision)

Experience Level: 4 to 6 Years 

Work location: Mumbai, Bangalore & Trivandrum 

Notice Period : Immediate Joiner(Apply if you can join before 15th Sept)

Role & Responsibilities: 

We are actively seeking a highly skilled and experienced QA Tester with specialized expertise in Computer Vision testing to join our dynamic Quality Assurance team at Quantiphi. This critical role involves working directly with our esteemed client, Disney, to ensure the highest standards of quality, accuracy, and reliability for cutting-edge computer vision models and AI-driven applications. The ideal candidate will be instrumental in defining robust QA strategies, designing rigorous test suites, and driving test automation across the entire Software Development Life Cycle (SDLC), from initial requirements gathering to post-production monitoring and continuous improvement. If you are a dedicated Quality Assurance Engineer passionate about AI/ML testing and delivering exceptional user experiences for a global brand like Disney, we encourage you to apply.

Key Requirements (Must-Haves):

Experience & Education:

• Bachelor’s degree in Computer Science, Engineering, or a closely related technical field.

• Minimum of 3+ years of experience in Software Quality Assurance (QA), Software Testing, or Test Engineering.

• At least 1 year of experience in a lead QA role or senior QA position, demonstrating strong leadership, team coordination, and mentorship capabilities within a QA team.

QA Methodologies & Lifecycle:

• Proven experience managing Quality Assurance processes within Agile and Scrum environments, including active participation in daily stand-ups, sprint planning, and retrospectives.

• Deep understanding of the Software Development Life Cycle (SDLC) and Software Testing Life Cycle (STLC).

• Extensive hands-on experience with both manual testing and automated testing (QAE), including designing and implementing test automation frameworks.

Test Strategy & Planning:

• Proficiency in defining comprehensive QA strategies, developing detailed test plans, creating effective test cases, and setting up appropriate test environments.

• Expertise in using test case management tools (e.g., Jira, Zephyr Scale, TestRail, Xray) for organizing, tracking, and reporting test artifacts.

• Ability to accurately estimate QA effort and create realistic testing timelines aligned with project delivery goals.

Automation & Scripting Skills:

• Hands-on experience with implementing and maintaining test automation frameworks.

• Proficiency in scripting or programming languages such as Python or shell scripting for developing test scripts and automating QA processes.

• Experience with API testing tools (e.g., Postman, Swagger) for validating backend services and integrations.

• Familiarity with unit testing and integrating automated tests into CI/CD pipelines.

Defect Management & Collaboration:

• Expertise in identifying, documenting, tracking, and managing bugs using industry-standard defect management systems (e.g., Jira, Bugzilla, Azure DevOps).

• Strong collaboration skills to work effectively with developers, product managers, and other stakeholders to reproduce issues, facilitate bug resolution, and ensure timely fixes.

• Ability to lead and conduct thorough root cause analysis for any post-production defects or critical issues.

Key Responsibilities:

Pre-Implementation Phase (Test Planning & Strategy):

• Collaborate with product owners and business analysts to thoroughly understand business requirements, user stories, and use cases for computer vision applications.

• Define and document the overall QA strategy, including detailed test plans, comprehensive test cases, and optimal test environments.

• Design and develop rigorous test suites specifically for computer vision models, focusing on object detection, image segmentation, model accuracy, and edge-case scenario handling under various conditions.

• Set up and manage QA tools, test data, and establish robust test automation frameworks (including unit testing).

• Automate QA processes, exploring and leveraging Agentic AI or other advanced automation techniques for enhanced efficiency.

• Provide accurate QA effort estimations and create realistic testing timelines aligned with project delivery goals.

• Participate in risk assessments and provide critical input on feature feasibility and quality implications.

• Ensure all requirements are testable, clear, complete, and unambiguous.

During Implementation Phase (Execution & Coordination):

• Lead and coordinate all day-to-day QA and testing activities, overseeing both manual testing and automated testing efforts.

• Execute a wide range of tests including functional testing, integration testing, regression testing, performance testing, and User Acceptance Testing (UAT) prior to production deployment.

• Identify, document, prioritize, and track bugs and defects using the agreed-upon defect management system.

• Work closely with developers to reproduce, debug, and ensure timely resolution of identified issues.

• Continuously monitor quality metrics, track progress, and provide regular status updates in team meetings and to stakeholders.

• Ensure all testing activities adhere to established QA processes, standards, and project timelines.

After App Implementation (Post-Release & Continuous Improvement):

• Conduct thorough post-release testing and smoke testing to verify successful deployments and production readiness.

• Monitor user feedback, system logs, and analytics for any potential undetected issues or anomalies in the production environment.

• Lead and facilitate root cause analysis for any post-production defects, ensuring lessons learned are applied.

• Drive continuous improvement initiatives for QA processes, methodologies, and tools based on performance metrics and feedback.

• Maintain and update test case documentation and automation scripts for future reuse and scalability.

• Continuously audit configuration settings and deployment pipelines to ensure system stability, reliability, and minimize downtime.

Good to Have:

• Familiarity with cloud platforms (e.g., AWS Lambda, AWS S3, AWS RDS) for testing data-related use cases and cloud-native applications.

• Knowledge of other programming languages (e.g., Java, Node.js, C#) for diverse testing environments.

• Experience with containerization technologies like Docker or Kubernetes for test environments.

• Understanding of data science pipelines and MLOps for AI model deployment.

Computer Vision Testing Experience:

• Demonstrated ability to design, develop, and execute rigorous test suites specifically for computer vision models and AI/ML applications.

• Proven experience in validating critical aspects such as object detection accuracy, image segmentation, facial recognition, and handling edge-case scenario testing across diverse lighting, environmental conditions, and data variations.

• Familiarity with AI/ML model validation and data quality testing.

Experience Level

Senior Level