Expert
Quality Assurance (QA) - Lead - MiDAS
Company Description
Bosch Global Software Technologies Private Limited is 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 Description
Roles & Responsibilities : AI Quality Strategy: Develop and own the evaluation framework for GenAI solutions, focusing on Faithfulness, Relevancy, and Hallucination detection using LLM-as-a-judge frameworks.
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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.
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Shift-Left Integration: Embed automated quality checks directly into GitHub Workflows, enabling seamless CI/CD.
Performance & Resilience: Lead JMeter-based performance testing.
Qualifications
Educational qualification:
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Experience: 8+ years in Software QA
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Problem Solving: Ability to define "quality" in an ambiguous, non-deterministic AI landscape.
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Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
Experience :
- 8+ years in Software QA
Mandatory/requires Skills : Automation & Tooling
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Python Mastery: Expert-level Python skills for building custom test tooling and automation scripts.
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Testing Stack: Hands-on proficiency with Pytest (API), Playwright (E2E), and JMeter (Performance).
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DevOps: Advanced experience designing and maintaining GitHub Actions/Workflows for automated test execution.
Core AI & LLM Expertise
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Learning 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).
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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.
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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.
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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 Information
Why Join MiDAS?
You won't just be testing software; you will be defining the quality standards for the future of AI-First Engineering. Your work will directly impact the speed and reliability of vehicle software development globally.
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