AI QA Engineer (GenAI & ML Systems)

Tasks

Job Description: 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

Qualifications

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 clear writer, and a close collaborator to AI engineers.

Experience: 4-8 years

Employment: Full-time