Senior Qa Engineer- AI Rendering

Autodesk Autodesk · Enterprise · Pune, India

Senior QA Engineer focused on AI Rendering features within a CAD/CAM/CAE platform. Responsibilities include leading AI validation strategies, ensuring model quality and reliability, developing automated testing frameworks for AI, architecting AI model validation strategies (accuracy, output quality, regression, consistency), developing quality metrics for AI outputs, validating AI model performance (latency, throughput, resource utilization), developing testing approaches for probabilistic and non-deterministic AI outputs, and collaborating with data science teams. Requires strong understanding of AI/ML concepts, model training, inference, evaluation metrics, and AI model validation techniques.

What you'd actually do

  1. Architect and execute AI model validation strategies, including accuracy testing, output quality assessment, regression testing, and consistency validation across model versions
  2. Develop scalable methodologies and quality metrics to evaluate AI-generated outputs across diverse datasets, edge cases, and rendering scenarios.
  3. Validate AI model performance characteristics, including latency, throughput, resource utilization, and behaviour across different hardware and deployment environments
  4. Develop testing approaches for AI-driven systems that address probabilistic outputs, non-deterministic behaviour, model drift, and evolving performance characteristics
  5. Collaborate closely with engineering, product, and data science teams to ensure data quality, model reliability, comprehensive validation coverage, and successful delivery of AI-powered features

Skills

Required

  • 5+ years of software QA experience
  • 2+ years focused on AI or ML applications
  • Strong understanding of AI and ML concepts including model training, inference, and evaluation metrics
  • Experience with AI model validation techniques and testing challenges including data drift, edge cases, and non-deterministic outputs
  • Proficiency in Python for test automation
  • Solid knowledge of software testing methodologies and defect tracking
  • Strong analytical, problem solving, and communication skills
  • Ability to lead initiatives and collaborate in cross functional teams
  • Understanding of model validation concepts including data quality assessment, edge case testing, and output reliability
  • Ability to critically analyse AI outputs and identify issues related to bias, non-determinism, or inconsistent results
  • Demonstrated mindset for testing AI driven systems where validation requires qualitative, statistical, and scenario-based testing approaches rather than deterministic validation

Nice to have

  • Experience in application testing with AI
  • Experience in agile software development process
  • Hands on experience with 3D CAD or CAE software such as Fusion, Inventor, CATIA, or similar tools
  • Experience with CI or CD pipelines and AI validation integration
  • Prior experience mentoring QA team members
  • Familiarity with AI testing frameworks, evaluation pipelines, or tools used for automated model validation
  • Understanding of responsible AI principles including bias detection, fairness validation, and output verification
  • Ability to align testing practices with organizations adopting AI as a major disruptor in modern software development

What the JD emphasized

  • AI model validation strategies
  • AI model performance characteristics
  • AI-driven systems
  • data quality
  • model reliability
  • comprehensive validation coverage
  • AI-generated outputs
  • non-deterministic behaviour
  • model drift
  • evolving performance characteristics
  • AI-enabled quality engineering processes

Other signals

  • AI model validation strategies
  • AI-generated outputs across diverse datasets
  • AI model performance characteristics
  • testing approaches for AI-driven systems
  • data quality, model reliability, comprehensive validation coverage