: AI QA Engineer (GenAI & ML Systems)

We hiring an AI QA Engineer to own the quality, reliability and safety of our GenAI and ML-driven engineering applications. You will define how we test non-deterministic systems, from prompts and RAG pipelines to agentic workflows, and set the release bar for AI features used across Powertrain Engineering.

Key Responsibilities

- Design evaluation frameworks for LLM, RAG and agentic systems: accuracy, hallucination, consistency, latency and cost
- Build golden datasets, regression suites and automated eval pipelines integrated into CI/CD
- Run red-teaming and adversarial tests: prompt injection, jailbreaks, data leakage, bias and toxicity
- Validate retrieval quality, tool-calling and multi-step agent behavior
- Define quality gates and release criteria for AI features
- Monitor production drift and failure modes; lead root-cause analysis with AI engineers
- Report quality metrics and risks to engineering leadership

Required Qualifications

- 4-8 years in QA/SDET,



including 1-2+ years testing ML or GenAI systems
- Strong Python and test automation (pytest, API testing)
- Hands-on with LLM evaluation tools such as Ragas, DeepEval, promptfoo or LangSmith
- Working knowledge of LLM behavior, RAG architectures, embeddings and prompt engineering
- Statistical thinking: sampling, metric design, confidence, and the limits of LLM-as-judge
- CI/CD (GitHub Actions or Azure DevOps), Git and cloud fundamentals (Azure preferred)

Preferred Qualifications

- Experience with agent frameworks and MCP/tool integrations
- Observability tooling for tracing and drift monitoring
- Exposure to engineering or automotive domains

Who Qualifies

Skeptical by default, precise, comfortable with non-deterministic outputs, a explicit writer, and a close collaborator to AI engineers.

Experience: 4-8 years

Employment: Full-time

📌 AI QA Engineer (GenAI & ML Systems) (Bengaluru)
🏢 Mercedes Benz Research and Development India
📍 Bengaluru