AI QA Architect

Experience Level:

Role Overview

technical depth, AI solution development skills, and architectural vision

• We are seeking an

  AI QA Architect

  with a strong background in Quality Engineering and hands-on expertise in applying

  Artificial Intelligence (AI) and Generative AI (GenAI)

  across testing workflows.
• The ideal candidate will architect, design, and implement

  AI-driven QA frameworks

  , build

  agent-based GenAI applications

  , and integrate intelligent automation solutions into enterprise QA ecosystems.

Key Responsibilities

• AI-Driven QA Strategy & Architecture

• Design and implement

  AI-powered QA frameworks

  integrating capabilities such as self-healing automation, intelligent defect prediction, test case generation, and root cause analysis.
• Define and execute the

  AI enablement roadmap

  for QA practices, leveraging LLMs, ML models, and automation accelerators.

• GenAI Application Development

• Develop

  Agent-based GenAI applications

  using frameworks such as

  LangChain, LlamaIndex, or Semantic Kernel

  .
• Enable

  multi-agent collaboration

  for test generation, failure triage, and defect clustering.

• AI Application Integration & Automation

• Build and integrate GenAI applications with

  GitHub Copilot

  , CI/CD pipelines, and test automation frameworks.
• Implement

  AI-based copilots

  to assist test engineers with code suggestions, scenario design, and defect classification.
• Host and deploy AI applications on

  cloud (Azure/AWS/GCP)

  or

  on-premises intranet environments

  , ensuring security and compliance.

• Hands-On Development & Implementation

• Develop AI/ML components using

  Python, JavaScript, or Java

  , integrating APIs from

  OpenAI, Gemini, Claude, or Azure OpenAI Service

  .
• Create and fine-tune LLM prompts and workflows for

  test artifact generation, requirement understanding, and automation script synthesis

  .

Required Skills & Experience

• Core QA Expertise:

• 10+ years of QA experience, with strong knowledge of

  automation frameworks, performance testing, and CI/CD integration

  .
• Proven experience architecting or scaling

  test automation frameworks

  (Selenium, Playwright, Cypress, etc.).

• AI & GenAI Skills:

• Hands-on experience in

  AI-based QA solutions

  self-healing, test generation, or intelligent analytics.
• Experience developing

  Agent-based GenAI apps

  using

  LangChain, LlamaIndex, AutoGen, or CrewAI

  .
• Strong understanding of

  prompt engineering, RAG workflows, and LLM model tuning

  .
• Experience integrating with

  GitHub Copilot

  or other AI developer assistants.

• Programming & Tools:

• Proficiency in

  Python

  (preferred),

  Java

  , or

  JavaScript

  for AI and automation development.
• Familiarity with

  OpenAI, Hugging Face, Gemini, or Anthropic APIs

  .
• Knowledge of

  Azure, AWS, or GCP

  for AI service deployment and monitoring.
• Experience using

  Docker, Kubernetes, and REST APIs

  for app deployment.

Preferred Qualifications

• Experience implementing

  AI-driven QA accelerators

  or

  GenAI copilots

  within enterprise testing environments.
• Knowledge of

  security, compliance, and governance

  for AI-enabled testing workflows.
• Contributions to open-source AI or QA projects will be an added advantage.
• Certifications in

  AI/ML, Cloud (Azure/AWS), or Advanced Test Automation

  preferred.