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Mid-level SDET title plus ML/LLM specialization narrows pool but still attracts many QA applicants.
Requires ML/LLM evaluation experience and model validation skills, limiting cross-industry transferability.
Explicit 5–8 years plus mandatory ML/LLM testing and automation skills creates strict selection filters.
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Own end-to-end quality assurance and validation for ML models, AI agents, and AI-powered features in the Automotive Retail Cloud platform.
Design and implement automated testing and evaluation frameworks for model inference, prompt pipelines, agent workflows, and related APIs.
Lead root cause analysis for production AI issues and establish quality metrics, guardrails, and automated monitoring for model performance.
5–8 years of experience in SDET, quality engineering, or software engineering with strong test automation framework skills.
Strong programming skills in Python and/or Java, capable of writing production-quality automation code.
Hands-on experience testing ML/AI systems or a strong software testing background with demonstrated ML/LLM fluency.
Solid understanding of ML concepts including training, inference, evaluation metrics, and non-deterministic model outputs.
Experienced in designing evaluation frameworks or working with evaluation datasets and LLM-as-judge approaches for AI quality validation.
Familiar with LLM/agent concepts such as prompting, retrieval augmented generation (RAG), embeddings, and generative failure modes like hallucination and drift.
Capable of collaborating effectively with ML Engineers, Data Scientists, and Product teams in a fast-paced ML engineering environment.