AI Test Engineer

Overview

In this role you will design and evolve a Python-based testing framework for generative AI agents and LLM-based systems. You’ll shape evaluation pipelines, observability, and data-driven testing, collaborating across AI, data engineering, and QA teams to improve reliability in production. You’ll implement synthetic/adversarial data generation and define quality KPIs to drive trustworthy AI behavior. This is a hands-on, independent role with a strong impact on how we validate AI systems at scale.

Responsibilities

• Design and develop Python-based testing frameworks for AI agents and LLM systems
• Contribute to architecture for evaluation pipelines, observability, and data-driven testing
• Build and maintain test data generation tools (synthetic/adversarial data)
• Define and implement evaluation strategies and QA KPIs (accuracy, robustness, bias, hallucinations)
• Integrate LLM evaluation tools, CI/CD, and external AI platforms
• Support continuous testing, benchmarking, and production monitoring of AI systems
• Collaborate with AI engineers, data engineers, and QA teams to improve reliability and scalability

Key requirements

• 2–5+ years of experience in Software Engineering, Test Automation, AI Engineering, or Data Engineering
• Strong Python proficiency and data/AI libraries (pandas, numpy, PyTorch or similar)
• Solid understanding of LLMs, NLP concepts, or generative AI systems
• Experience with test automation frameworks (PyTest, Playwright) and CI/CD
• Familiarity with data pipelines, dataset design, or feature engineering
• Experience with evaluation metrics for NLP/AI systems (BLEU, ROUGE, embedding-based metrics)
• Ability to design scalable systems and work autonomously on complex problems
• Strong analytical mindset and interest in AI system quality, reliability, and governance

• autonomous work style
• collaboration with cross-functional teams
• analytical mindset
• Python
• pandas
• numpy

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