QA Lead

Nura Analytics; Chennai District; On-site; INR 1,200,000 - 1,500,000; Full time

Job summary

Nura Analytics is seeking a skilled AI/GenAI Testing professional (QA Lead) in Chennai, Tamil Nadu, who will ensure quality and reliability in AI-driven applications. This role involves leading QA strategies, designing test plans for LLM validation, and collaborating on UAT.

The ideal candidate has 8+ years of experience with a strong background in enterprise application testing and AI automation workflows. Immediate joiners are preferred, and the position includes opportunities for mentorship and process improvement.

Qualifications

• 8+ years of experience with at least 3 years in relevant fields.
• Strong experience in testing enterprise applications and AI automation workflows.

Responsibilities

• Lead the end-to-end quality assurance strategy for AI projects.
• Design and execute comprehensive test plans for AI/GenAI solutions.
• Validate AI outputs for extraction accuracy and edge-case handling.
• Build robust API test suites using Postman and Python frameworks.
• Develop automation test frameworks for regression and end-to-end coverage.
• Lead performance testing initiatives using JMeter.
• Design security testing protocols for AI-driven systems.
• Implement human-in-the-loop validation workflows.
• Define and track AI model evaluation metrics.
• Embed QA processes into CI/CD pipelines.
• Manage defect lifecycle in Jira.
• Collaborate with development and business teams for UAT coordination.
• Mentor QA engineers and establish team standards for testing.

Skills

Manual Testing

Automation Testing

API Testing

AI/GenAI Testing

LLM Validation

Prompt Testing

OCR Validation

Performance Testing

Security Testing

Selenium

Postman

JMeter

Python

CI/CD

Defect Management

Test Planning

Regression Testing

Model Accuracy Validation

Jira

Test Automation Frameworks

Job description

We are seeking an experienced AI/GenAI Testing professional (QA Lead) to ensure the quality, reliability, and security of enterprise AI-driven applications. The ideal candidate should have expertise in API and automation testing, AI model evaluation metrics, performance and security testing, and human-in-the-loop validation to ensure robust and scalable AI solutions.

Experience

8 yrs with relevant experience of 3 yrs

Immediate Joiners Preferred

Key Responsibilities

• Lead the end-to-end quality assurance strategy for enterprise AI and application projects, including test planning, execution, defect management, and sign-off.
• Design and execute comprehensive test plans for AI/GenAI solutions covering LLM response validation, prompt behaviour testing, hallucination detection, bias evaluation, and output accuracy.
• Validate OCR and Document AI outputs for extraction accuracy, field mapping correctness, and edge‑case handling across diverse document types and formats.
• Build and maintain robust API test suites using Postman and Python‑based frameworks; ensure all integration points, endpoints, and data contracts meet functional and non‑functional requirements.
• Develop and maintain automation test frameworks using Selenium and other relevant tools for regression, smoke, and end‑to‑end test coverage across enterprise applications.
• Lead performance and load testing initiatives using JMeter and similar tools to validate system behaviour under peak conditions and establish performance baselines.
• Design and execute security testing protocols, including vulnerability assessments, injection testing, and access‑control validation for AI‑driven systems.
• Implement and manage human‑in‑the‑loop (HITL) validation workflows to ensure AI model outputs are reviewed, corrected, and fed back into model improvement cycles.
• Define and track AI model evaluation metrics such as precision, recall, F1 score, hallucination rate, and extraction accuracy, and report on model quality trends over time.
• Embed QA processes into CI/CD pipelines, implementing automated testing gates that prevent defective builds from progressing to higher environments.
• Manage defect lifecycle end‑to‑end in Jira from identification and classification through root‑cause analysis, resolution tracking, and closure verification.
• Collaborate with development, AI engineering, and business teams to define acceptance criteria, review test cases, and ensure thorough UAT coordination and sign‑off.
• Mentor QA engineers, establish team standards for test automation and AI testing, and drive continuous improvement in testing tools, processes, and coverage.

Mandatory Requirements

• Should have strong experience in testing both enterprise application projects and AI automation workflows.

Required Skills

• Manual Testing
• Automation Testing
• API Testing
• AI/GenAI Testing
• LLM Validation
• Prompt Testing
• OCR Validation
• Performance Testing
• Security Testing
• Selenium
• Postman
• JMeter
• Python
• CI/CD
• Defect Management
• Test Planning
• Regression Testing
• Model Accuracy Validation
• Jira
• Test Automation Frameworks

Preferred Qualifications

• Strong communication and stakeholder management skills.
• Ability to work in a fast‑paced, outcome‑driven environment.
• Experience in enterprise‑scale delivery and collaboration.