Job Overview
Location
Pune
Job Type
Full-time
Category
Software Engineering
Date Posted
May 26, 2026
Full Job Description
đź“‹ Description
- • Build and own end-to-end test automation for the platform to enable continuous quality assurance, leveraging AI agents and cutting-edge technologies to optimize testing efficiency and coverage.
- • Own responsibility for achieving team quality targets: ensuring 80% of quality issues are caught during the development cycle via automated tests, with manual testing reserved exclusively for 20% of edge cases.
- • Ensure 90% of customer-facing releases and patches are shipped without any quality-related delays.
- • Establish and maintain a prioritized QA roadmap aligned with VP Engineering objectives, focusing the QA team on highest-risk areas with clear ownership and measurable outcomes.
- • Implement a standardized, shared quality playbook adopted across 100% of product areas, documenting defined risk categories, acceptance criteria, and test requirements for each domain.
- • Reduce average bug resolution time by 30% through joint triage sessions, severity evaluation, and validation processes involving cross-functional teams.
- • Collaborate closely with developers, product managers, and engineering leadership to communicate quality risks, influence product decisions, and ensure user-centric reliability.
- • Translate complex technical behaviors into actionable, precise bug reports that drive rapid resolution and prevent recurrence.
- • Continuously innovate in test automation by evaluating and integrating AI-driven and agentic tools to enhance test creation, execution, and maintenance.
- • Maintain deep technical proficiency in automation frameworks, CI/CD pipelines, Python scripting, and Playwright-based automations for web and application testing.
- • Conduct software performance testing to validate system behavior under load and ensure scalability of enterprise-grade features.
- • Apply field knowledge in Mechanical Engineering, Physics, and Machine Learning to understand simulation and CAE/CAD/PLM system behaviors and design relevant test scenarios.
- • Foster a builder mindset by proactively identifying gaps in quality processes, proposing solutions under uncertainty, and pushing boundaries of what’s possible in product validation.
- • Cultivate a strong team spirit by mentoring peers, sharing best practices, and embedding quality ownership across all product teams.
- • Act as a bridge between user needs and product development by validating onboarding journeys, technical documentation accuracy, and feature value communication.
- • Ensure all quality practices are aligned with the mission of empowering users to adopt, master, and advocate for the platform through reliable, intuitive experiences.
🎯 Requirements
- • 6+ years of strong QA experience in complex technical software (CAE, CAD, PLM, simulation, or similarly demanding systems)
- • Applied depth in AI and agentic tools for QA and test automation, with ability to use them thoughtfully for meaningful impact
- • Solid technical foundations in automation, CI/CD, Python scripting, Playwright automations, and software performance testing
- • Field knowledge in Mechanical Engineering, Physics, and Machine Learning
- • Proven ability to reason deeply about product behavior to influence QA decisions and collaborate seamlessly with developers and leadership
- • Ownership of problems end-to-end with responsibility for the quality of what users ultimately experience
🏖️ Benefits
- • Opportunity to work at the intersection of product and community, shaping user empowerment through quality and learning
- • Exposure to cutting-edge AI and agentic tools in test automation
- • Collaborative, builder-focused culture with autonomy to innovate and push boundaries
- • Inclusive, diverse environment committed to employee growth and thriving
Skills & Technologies
About Neural Concept SA
Neural Concept SA provides cloud-native AI software that accelerates engineering simulations for automotive, aerospace and industrial-product teams. Its core product, Neural Concept Shape, learns from existing CAD and CAE data to predict aerodynamics, structural and thermal performance in seconds, replacing hours of traditional solving. The EPFL-spinoff’s 3D deep-learning models integrate into standard design workflows, enabling rapid shape optimization and lightweighting without expert CFD or FEA setup. Customers include Airbus, Bosch, Subaru and Formula 1 suppliers. Founded in 2018 and headquartered in Lausanne, Switzerland, the company serves global OEMs through subscription licenses and secure cloud deployments.
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