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AI Lead

Myridius x Aethereus

1 - 2 years

3 - 4 Lacs

chennai

Posted:22 hours ago| Platform:

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Skills Required

computer science environment management automation github data management test scripts test strategy jira continuous improvement sdlc

Work Mode

Work from Office

Job Type

Full Time

Job Description

Preferred Qualifications:

Bachelor s degree in Computer Science , Engineering, or a related field.

10 + years of experience in software testing and 5 + years in managing Automation QA teams.

Strong knowledge of Agile/Scrum methodologies and tools (e.g., JIRA, Confluence, or Azure DevOps).

Certifications in software testing or quality management (e.g., ISTQB, CSM)

Prompt Engineering for QA: 1 2 years creating optimized prompts for AI test generation.

Custom AI Model Tuning: 1 2 years fine-tuning LLMs for QA-specific scenarios.

API Testing: 3+ years with tools such as Postman, REST Assured, or similar.

Generative AI for Documentation: 1 2 years using AI tools to generate or maintain QA documentation.

Test Data Management: 2+ years using synthetic test data generation, including AI-assisted data creation.

Duties and Responsibilities

AI-Driven Test Strategy & Roadmap

Define and execute the AI adoption roadmap for Quality Engineering, focusing on intelligent test generation, self-healing automation, and defect prediction.

Evaluate and select AI-powered QA tools and frameworks (e.g., GitHub Copilot, Cursor, TestRigor , Tricentis AI accelerators, Mabl ).

Establish a balanced strategy blending traditional test automation with AI-driven approaches.

AI/Automation Implementation

Configure, pilot, and optimize AI-based test tools for functional, regression, performance, and API testing.

Collaborate with tool partner and delivery teams to develop AI-assisted accelerator and assets for all phase of test life cycle including test data generation, defect log analysis, root cause detection.

Ensure AI-generated test artifacts meet reliability, maintainability, and compliance standards.

Test Automation Enablement

Lead the adoption of AI-assisted automation for functional, regression, performance, database , and API testing.

Integrate AI-enabled automation into multi-channel platforms (web, mobile, API, data pipelines, cloud-native).

Ensure automation scalability in CI/CD pipelines, containerized environments, and cloud platforms (AWS, Azure, GCP).

Test Governance

Establish standards and best practices for AI-generated test scripts to ensure reliability, maintainability, and compliance.

Implement validation processes for AI outputs to detect bias, errors, and gaps.

Define usage guidelines and governance policies for QA teams leveraging AI tools.

Training & Mentorship

Conduct training sessions, workshops, and PoCs to accelerate team adoption of AI-powered QA practices.

Mentor QA engineers to embed AI tools in daily workflows and promote AI-first quality culture.

Create knowledge repositories and playbooks for AI in QA.

Tool Evaluation & Continuous Innovation

Continuously evaluate emerging AI testing tools, frameworks, and methodologies, benchmarking their effectiveness.

Drive proof-of-concepts (POCs) to assess feasibility and ROI of AI solutions.

Influence organizational testing roadmap by introducing next-gen AI practices.

Cross-Team Collaboration

Partner with Development, DevOps, Product, and Security teams to seamlessly integrate AI-assisted QA into SDLC.

Ensure AI-assisted automation aligns with regulatory and security requirements.

Test Data & Environment Management (Expanded Scope)

Define strategies for AI-powered test data generation, anonymization, and synthetic data creation.

Integrate test data provisioning into automation and CI/CD workflows.

Collaborate with environment teams to ensure AI-driven automation works across all test stages (Dev, QA, UAT, Prod-like).

Performance Measurement & Reporting

Define and track AI-specific QA KPIs, such as:

Productivity gains from AI-assisted automation.

Reduction in test creation and execution effort.

Improvement in defect detection rate, coverage, and defect leakage.

Build dashboards and automated reporting mechanisms to monitor AI s impact on quality.

Continuous Improvement & Thought Leadership

Foster a culture of innovation by promoting experimentation with AI in QA.

Contribute to organizational best practices, white papers, or CoE initiatives on AI testing.

Stay updated with industry trends in AI/ML, automation frameworks, and QA practices to proactively evolve capabilities.

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