Description

Key Responsibilities

AI Strategy & Testing Transformation

• Define and lead the AI vision and strategy for enterprise testing organizations
• Establish AI adoption roadmaps and best practices for QA teams
• Drive AI-enabled transformation initiatives across testing and quality engineering functions
• Identify opportunities where AI can improve testing efficiency, coverage, and quality

AI-Driven Testing Solutions

Design and implement AI-powered solutions for:

• Test case generation
• Test data creation
• Automated test maintenance
• Defect prediction
• Root cause analysis
• Failure and log analysis
• Intelligent regression testing
• Risk-based test prioritization
• AI-assisted automation workflows

Framework & Architecture Design

Design scalable AI-enabled testing frameworks supporting:

• Functional testing
• API testing
• UI automation and validation
• Performance testing
• Security testing support

QA Engineering & DevOps Integration

• Integrate AI capabilities into CI/CD pipelines and quality engineering workflows
• Collaborate with QA, Engineering, Product, and DevOps teams
• Support enterprise-scale testing modernization initiatives
• Promote AI-assisted development and testing methodologies

Leadership & Enablement

• Mentor QA engineers and automation teams on AI-assisted testing practices
• Lead proofs of concept, pilot programs, and enterprise rollouts
• Establish standards and governance for AI adoption within QA organizations

Required Qualifications

Education

Bachelor’s or Master’s degree preferred in:

• Computer Science
• Engineering
• Data Science
• Related technical disciplines

Experience

• 10 years of overall industry experience
• Strong background in:
  • Software Testing
  • QA Automation
  • Quality Engineering
  • Test Architecture
• Minimum 3+ years of experience implementing:
  • Artificial Intelligence solutions
  • Machine Learning systems
  • Generative AI solutions
  • Enterprise AI initiatives

Note: Machine learning experience may contribute toward the AI requirement; however, candidates must demonstrate direct exposure to AI/GenAI technologies.

Required Technical Skills

AI / Machine Learning

Hands-on experience with:

• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Machine Learning
• Prompt Engineering
• Enterprise AI implementations
• AI-assisted workflows and automation

Programming Languages

Highest Priority

• Java
• SQL

Additional Preferred Languages

• Python
• JavaScript

Testing Tools & Frameworks

Top Required Tools

• Selenium
• Playwright

Additional Preferred Tools

• Karate
• TestNG
• API testing frameworks/tools
• Tosca (preferred)

Quality Engineering & SDLC Knowledge

Strong understanding of:

• SDLC (Software Development Life Cycle)
• STLC (Software Testing Life Cycle)
• QA operations and testing methodologies
• Automation frameworks
• CI/CD pipelines
• Quality engineering best practices

Preferred Qualifications

• Experience building AI copilots or AI-assisted QA solutions
• Experience integrating AI into enterprise testing environments
• Familiarity with:
  • Retrieval-Augmented Generation (RAG)
  • AI agents
  • Workflow automation
  • Observability and log analysis tools
  • Scalable enterprise architecture design
• Experience working in Agile/Lean environments
• Knowledge of DevOps and modern engineering practices
• Exposure to cloud platforms such as AWS, Azure, or GCP

Key Skills

Education

Bachelor's degree