Data quality engineer for AI systems

QuantumLight Capital is looking for an analytical and technically strong Data Quality Specialist to improve the quality of data and evaluation environments powering our AI systems. You will work at the intersection of data quality, agent engineering and evaluation, building reliable datasets, designing validation mechanisms and supporting the development of robust RL environments. You will collaborate closely with the Data and Investment Teams to ensure our systems produce accurate, consistent and reliable outputs.

• Build and maintain data validation processes to ensure the accuracy, completeness and consistency of large datasets
• Develop evaluation datasets, test cases and quality checks to assess AI agent performance, identify failure modes and improve reliability
• Support the creation and maintenance of structured environments for reinforcement learning, including scenarios, test cases and feedback mechanisms
• Identify inconsistencies, conflicting information and unexpected model or data behaviour, investigating root causes and implementing fixes
• Use SQL and Python to build validation scripts, automate quality checks and improve data processing and evaluation workflows
• Work with Data and Investment Teams to translate business requirements into reliable datasets, evaluation criteria and testing frameworks

• 3–4 years of experience in data quality, data engineering, data analysis or a related technical field
• Strong proficiency in Python and SQL
• Experience working with large datasets, data validation and automated testing
• Understanding of AI/ML concepts and experience evaluating model outputs or AI system performance
• Familiarity with agent-based systems, evaluation frameworks or reinforcement learning environments
• Experience with Git and version control
• Strong analytical and problem-solving skills, with exceptional attention to detail
• Ability to investigate complex issues, identify root causes and develop systematic solutions
• Будет плюсом: Experience with Spark; Demonstrated interest in venture capital and its data-driven applications

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