AI QA Engineer (Multilingual)

Key Responsibilities

• Inspect, review, and grade LLM training data, evaluation test cases, and model outputs to ensure quality and accuracy.
• Maintain local development environments, run test pipelines, investigate edge cases, and submit Git/GitHub PRs.
• Analyze training data to identify error cases and technical failures.
• Leverage LLMs for translation, verification, and maintenance of cross-lingual datasets.
• Collaborate with engineering teams to refine evaluation criteria and improve data pipelines.

Key Qualifications

• Strong technical background with hands-on coding experience (Python preferred) and proficiency with Git/GitHub.
• Fluency in English and native or near-native proficiency in at least one other language.
• Deep understanding of Large Language Models (LLMs), failure modes (hallucinations, formatting errors), and prompting techniques.
• Promising experience in Quality Assurance, Data Quality, or Data Engineering, with a track record of auditing large datasets.
• Exceptional written communication skills across multiple languages.

Required Skills & Attributes

• Obsessive attention to detail for finding edge cases and translation errors.
• Ability to handle repetitive data inspection tasks with a "builder" mentality.
• Techical self-sufficiency: comfort with terminal usage, Python scripts, and version control.
• Strong linguistic understanding of nuances required for high-quality cross-lingual evaluation.
• Ability to thrive in a fast-paced, ownership-driven environment.