Job Requisition ID #
26WD94632
Position Overview
Fusion is a cloud-based 3D modeling, CAD, CAM, CAE, and PCB software platform for professional product design and manufacturing. We're seeking an experienced Senior Software QA Engineer to join the Design Engineering QA team, focusing on Modelling capabilities and AI features. This role will lead AI validation strategies, ensure model quality and reliability, and provide technical leadership for the team. This is a hybrid position in Pune, India. You will report to QA Manager.
Responsibilities
- Participate in Agile/Scrum ceremonies including sprint planning, daily stand-ups, backlog refinement, sprint reviews, and retrospectives to gather requirements, estimate effort, plan testing activities, and communicate progress
- Develop, maintain, and execute comprehensive test strategies, test plans, and test cases covering functional, regression, integration, performance, workflow, and build verification testing
- Design, develop, and maintain automated testing frameworks and scripts for UI, API, system, and AI validation testing to improve test coverage and efficiency
- Perform both manual and automated testing across product releases to ensure product quality, reliability, and stability
- Identify redundant test scenarios and drive automation initiatives to optimize testing efforts and increase coverage
- Architect and execute AI model validation strategies, including accuracy testing, output quality assessment, regression testing, and consistency validation across model versions
- Develop scalable methodologies and quality metrics to evaluate AI-generated outputs across diverse datasets, edge cases, and rendering scenarios.
- Validate AI model performance characteristics, including latency, throughput, resource utilization, and behaviour across different hardware and deployment environments
- Develop testing approaches for AI-driven systems that address probabilistic outputs, non-deterministic behaviour, model drift, and evolving performance characteristics
- 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
- Investigate, reproduce, and validate customer-reported issues; perform root cause analysis in collaboration with development teams and verify fixes
- Serve as a quality advocate and champion, promoting best practices, continuous improvement, shared ownership of quality, and AI-enabled quality engineering processes
- Communicate quality status, risks, test results, and recommendations effectively to stakeholders, ensuring transparency throughout the development lifecycle
- Identify and proactively address risks, blockers, and issues that may impact project timelines, quality, or deliverables
- Support customers by providing feedback on product quality, usability, training-related issues, and reported defects
- Maintain accountability for project deliverables, deadlines, and quality objectives while demonstrating self-motivation and ownership
- Mentor team members, lead by example, and contribute to the growth of quality engineering practices across the organization
- Continuously adapt QA processes, validation strategies, and automation frameworks to support the speed, scale, and complexity of modern AI-enabled development cycles
Minimum Qualifications- Bachelor's degree in mechanical, computer science, Data Science, AI, or related field
- 5+ years of software QA experience, with 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
Preferred Qualifications- 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
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