Quality Assurance (QA) - Lead - MiDASFull-timeLegal Entity: Bosch Global Software Technologies Private LimitedCompany DescriptionBosch Global Software Technologies Private Limitedis a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 27,000+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.Job DescriptionRoles & Responsibilities :AI Quality Strategy:Develop and own the evaluation framework for GenAI solutions, focusing onFaithfulness, Relevancy, and Hallucination detectionusing LLM-as-a-judge frameworks.Hybrid Test Automation:Architect a dual-layered automation suite:Deterministic:E2E UI (Playwright) and API testing (Pytest/Requests).Probabilistic:Automated evaluation of non-deterministic LLM outputs.Shift-Left Integration:Embed automated quality checks directly intoGitHub Workflows, enabling seamless CI/CD.Performance & Resilience:Lead JMeter-based performance testing.QualificationsEducational qualification:Experience:8+ years in Software QAProblem Solving:Ability to define "quality" in an ambiguous, non-deterministic AI landscape.Education:Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.Experience :8+ years in Software QAMandatory/requires Skills :Automation & ToolingPython Mastery:Expert-level Python skills for building custom test tooling and automation scripts.Testing Stack:Hands-on proficiency withPytest(API),Playwright(E2E), andJMeter(Performance).DevOps:Advanced experience designing and maintainingGitHub Actions/Workflowsfor automated test execution.Core AI & LLM ExpertiseLearning Agility in GenAI: High capability and interest in rapidly mastering AI evaluation concepts. You should be prepared to quickly upskill in automated metrics for LLMs (such as Faithfulness, Relevancy, and Groundedness).Exposure to LLM Logic: Basic familiarity with how LLMs function (e.g., prompting, context windows). You should be comfortable exploring and implementing "LLM-as-a-Judge" strategies, where high-reasoning models help grade application-specific outputs.Orientation toward RAG Systems: Interest in understanding the mechanics of Retrieval-Augmented Generation (RAG). You will be responsible for defining how we validate the accuracy of data retrieved from our engineering context catalogues and vector databases.Data-Driven Quality Mindset: A strong desire to move beyond binary "Pass/Fail" results toward probabilistic quality monitoring, utilizing tools like Langfuse to analyze live traces and performance trends.Preferred Skills :Additional InformationWhy Join MiDAS?You won't just be testing software; you will be defining the quality standards for the future ofAI-First Engineering. Your work will directly impact the speed and reliability of vehicle software development globally.By clicking the link above or any third-party link within this posting, you are leaving this site and going to a third-party website where the third-party website's terms and privacy policy applyI'm interestedI'm interestedPrivacy NoticeImprint
