Quality Intelligence Engineer AI-Powered Quality Engineering & Agent Validation

Role Overview The Quality Intelligence Engineer is responsible for designing, building, and operationalizing AI-powered quality engineering capabilities that accelerate testing and improve confidence in AI-driven solutions. This role focuses on the creation of intelligent testing assets, automated validation frameworks, and quality assurance processes for AI and agentic systems.

The Quality Intelligence Engineer ensures that applications, agents, and AI-enabled workflows are reliable, secure, scalable, explainable, and compliant with enterprise quality standards. Working closely with AI Engineers, Forward Deployed Engineers, and Solution Architects, this role establishes end-to-end quality practices across both traditional and AI-native solutions.

Key Responsibilities

AI-Powered Test Engineering

- Leverage AI and LLM-powered tools to generate and maintain test cases, test scripts, test data, test scenarios, and test documentation
- Build intelligent test generation frameworks that derive testing artifacts from requirements, user stories, process models, designs, and code
- Continuously optimize testing assets using AI-assisted analysis of defects, production issues, and usage patterns

AI & Agentic Solution Validation

- Design and execute test strategies for LLM-based, agentic, and multi-agent systems
- Validate agent reasoning, decision-making, workflow execution, tool usage, and action outcomes
- Develop evaluation frameworks to measure accuracy, consistency, robustness, hallucination rates, and business outcome alignment

Test Automation & Quality Platforms

- Build and maintain automated functional, integration, regression, performance, and security testing frameworks
- Integrate AI-powered testing capabilities into enterprise CI/CD pipelines




- Develop reusable quality engineering components, accelerators, and testing patterns for AI-centric applications

Non-Functional & Responsible AI Testing

- Validate AI systems for latency, scalability, reliability, resiliency, and cost efficiency
- Perform testing for prompt injection, data leakage, security vulnerabilities, bias, and responsible AI compliance
- Implement continuous monitoring, drift detection, and quality assurance controls for production AI solutions

Collaboration & Continuous Improvement

- Collaborate with AI Engineers to define quality gates throughout the development lifecycle
- Partner with Solution Architects to align testing approaches with enterprise architecture and governance standards
- Contribute reusable testing frameworks, evaluation methodologies, and best practices across the organization

Required Qualifications

- Solid experience in Quality Engineering, Test Automation, or Software Testing
- Hands-on experience with automated testing frameworks (playwright, selenium, etc) and CI/CD integration
- Proficiency in scripting or programming languages (e.g., Python, Java, JavaScript, TypeScript)
- Experience developing automated test cases, regression suites, and integration testing frameworks
- Understanding of AI/ML systems, LLM-based applications, or intelligent automation solutions
- Ability to define and execute quality assurance practices in complex enterprise environments

Preferred Qualifications

- Experience testing AI, GenAI, LLM, RAG, or agentic solutions
- Familiarity with AI-assisted testing tools and intelligent test generation techniques
- Knowledge of prompt engineering, model evaluation, and AI benchmarking methodologies
- Experience with performance testing, security testing, and resilience testing of distributed systems
- Understanding of Responsible AI, governance, explainability, and risk management frameworks

📌 Quality Intelligence Engineer _AI-Powered Quality Engineering & Agent (Hyderabad)
🏢 Capgemini
📍 Hyderabad