Quality Assurance (QA) - Lead - MiDAS
Summary
Lead QA engineer defining AI evaluation frameworks and hybrid test automation (Playwright, Pytest, JMeter) for Bosch’s AI-first vehicle software, integrating quality checks into GitHub CI/CD.
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.