Quality Assurance (QA) - Lead - MiDAS

Bosch Global Software Technologies · Bangalore

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Last observed by our collector: 8 October 2026 at 11:12 pm IST. This is not a manual review or a confirmation from the employer.

Employer requisition: REF292162X

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Collected minimum qualification: Educational qualification: Experience: 8+ years in Software QA Problem 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 QA Mandatory/requires Skills : Automation & Tooling Python Mastery: Expert-level Python skills for building custom test tooling and automation scripts. Testing Stack: Hands-on proficiency with Pytest (API), Playwright (E2E), and JMeter (Performance). DevOps: Advanced experience designing and maintaining GitHub Actions/Workflows for automated test execution. Core AI & LLM Expertise 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). 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 :Attendance and availability: I checked the employer page for work location, office attendance, start date and duration.Current requisition: I checked the current employer requisition and its application requirements.

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Collected role 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. 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. 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 into GitHub Workflows , enabling seamless CI/CD. Performance & Resilience: Lead JMeter-based performance testing. Educational qualification: Experience: 8+ years in Software QA Problem 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 QA Mandatory/requires Skills : Automation & Tooling Python Mastery: Expert-level Python skills for building custom test tooling and automation scripts. Testing Stack: Hands-on proficiency with Pytest (API), Playwright (E2E), and JMeter (Performance). DevOps: Advanced experience designing and maintaining GitHub Actions/Workflows for automated test execution. Core AI & LLM Expertise 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). 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 : 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.

Collected minimum qualification

Educational qualification: Experience: 8+ years in Software QA Problem 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 QA Mandatory/requires Skills : Automation & Tooling Python Mastery: Expert-level Python skills for building custom test tooling and automation scripts. Testing Stack: Hands-on proficiency with Pytest (API), Playwright (E2E), and JMeter (Performance). DevOps: Advanced experience designing and maintaining GitHub Actions/Workflows for automated test execution. Core AI & LLM Expertise 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). 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 :

Collected experience requirement

8 years