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Sr. Quality Analyst with 3+ years in AI/ML & GenAI testing.
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Results-driven Quality Analyst (Manual + Automation) | AI/ML & GenAI with ~2.5 years of experience in software testing, GenAI model validation, and data annotation QA. Expertise in test case design, defect lifecycle management, API testing, Selenium WebDriver, and automation frameworks (basic), along with LLM evaluation, prompt testing, and KPI-driven quality assurance. Proven track record of improving product quality through structured testing, guideline adherence, CI/CD awareness, and data-driven insights, achieving measurable improvements in accuracy and defect reduction.
SRM University, Chennai
B.Tech · Electronics & Communication Engineering
August 1, 2023 – June 30, 2023
Innodata
Quality Analyst – AI/ML & GenAI
June 1, 2023 – March 1, 2026
Noida, Uttar Pradesh, India
Innodata
AI/ML QA Intern
January 1, 2023 – May 1, 2023
Noida, Uttar Pradesh, India
GenAI Model Testing & Data Quality Assurance
January 1, 2023 – June 11, 2026
• Tested LLM responses for correctness, relevance, and safety, ensuring compliance with AI quality standards. • Designed and validated test scenarios for prompt-based AI systems. • Performed dataset QA using annotation guidelines and KPIs. • Identified edge cases, inconsistencies, and model errors. • Improved model performance through QA feedback and data refinement.
Software Testing Fundamentals
Unknown
June 11, 2026 – Present
Data Analytics (Excel & Power BI)
Unknown
June 11, 2026 – Present
Generative AI Fundamentals
Unknown
June 11, 2026 – Present
Cultural Fit Analysis
The candidate's experience in AI/ML and GenAI QA, coupled with certifications in Generative AI Fundamentals and Software Testing Fundamentals, aligns well with a forward-thinking, technology-driven culture. Their project diversity, including a personal project on GenAI Model Testing, shows initiative and a genuine interest in the field. The role at Innodata as a Quality Analyst focusing on AI/ML & GenAI directly matches the target role of Sr. Quality Analyst, indicating a strong cultural and operational fit for organizations working with advanced AI technologies.
Soft Skills & Operational Fit
The candidate demonstrates strong collaboration skills through working with developers, data scientists, and product teams in Agile sprints. Their experience in root cause analysis and continuous quality improvement indicates a proactive and problem-solving mindset. The focus on KPI monitoring and achievement suggests a data-driven and results-oriented approach.