## 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. - 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. ## Qualifications 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 : ## 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.