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QA Automation Engineer with 2+ years in AI/ML System Testing & Model Validation
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2.6 years of experience in end-to-end test automation with growing specialization in AI/ML system testing, model validation, and intelligent automation frameworks using Playwright and TypeScript. Hands-on experience testing AI-powered features including self-healing locators, intelligent test generation, LLM-based UI components, and model-driven workflows. Proficient in validating AI/ML model outputs—classification accuracy, regression predictions, NLP responses, and recommendation systems through automated pipelines.
Rashtrasant Tukadoji Maharaj Nagpur University
Bachelors Degree · Engineering
August 1, 2020 – June 30, 2023
Golden Petals Technologies Pvt Ltd
Software Engineer – AI/ML Test Automation
September 1, 2024 – Present
Pune, Maharashtra, India
Pace Business Machines Pvt. Ltd.
Back Office Executive
October 1, 2023 – August 1, 2024
Mumbai, Maharashtra, India
Telenet
April 1, 2025 – Present
Telenet is one of Belgium's largest telecom providers offering internet, TV, and mobile services. The project involved testing AI-augmented OSS/BSS systems, intelligent service provisioning workflows, and ML-driven customer experience features.
A&H Application
September 1, 2024 – April 1, 2025
A&H Application is an insurance platform used for policy lifecycle management including issuing, endorsing, reinstating, and canceling policies, with AI features for risk scoring, underwriting automation, and intelligent document processing.
Digital Banking Platform
October 1, 2023 – August 1, 2024
The project involved testing ML-powered fraud detection models integrated into the banking platform, validating real-time transaction scoring, false positive rates, and model drift indicators. Validated AI-driven personalization features including intelligent product recommendations and spending insights, testing output relevance and consistency across user segments. Built and maintained automation frameworks using Playwright and Selenium for banking workflows enhanced with AI decision support features. Automated API tests for ML model serving endpoints, validating inference accuracy, response latency SLAs, schema contracts, and graceful degradation under failure. Developed data validation scripts to verify training data integrity in feature stores and catch upstream data quality issues before model retraining. Executed model regression tests post-release to validate that fraud detection and recommendation performance did not degrade after model updates. Conducted performance testing with JMeter on AI inference services to ensure banking platform stability under high-load scenarios. Worked in Agile sprints alongside data engineers, ML engineers, and developers, participating in model review sessions and contributing to AI quality acceptance criteria.
Cultural Fit Analysis
The candidate's project diversity across Telecom, Insurance, and Banking sectors, all involving AI/ML-powered systems, indicates adaptability and a broad understanding of different business contexts. Their role as 'Software Engineer – AI/ML Test Automation' directly aligns with the target role of 'QA Automation Engineer' with a strong emphasis on AI/ML. The breadth of technical skills (Playwright, Selenium, JMeter, Postman, TypeScript, Java, Python, BDD, CI/CD) further supports a good cultural fit for a dynamic, technically advanced team. The experience with Model Context Protocol (MCP) and intelligent test generation shows an innovative mindset.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit for a QA Automation Engineer role, particularly with an AI/ML focus. Their experience in Agile environments, defect management (Jira/X-Ray), and collaboration with data scientists and developers indicates good team integration potential. The detailed project descriptions suggest a methodical approach to testing and problem-solving. However, without specific psychometric or communication test results, soft skills like logical reasoning, work attitude, stress handling, and team collaboration cannot be objectively assessed beyond what is inferred from project descriptions.