Quality Assurance (QA) - Lead - MiDAS
Bosch•2h ago
bengaluru, inHybridFull-timeSenior Level8+ yrs exp
Top focus
Qa EngineerQa Manager
- 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
- 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
- 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)
- 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
- 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
- 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.
Required skills
PythonPytestPlaywrightJMeterGitHub Actions