Job Description
Job Requisition ID #
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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