AI/ML Quality Engineering

QA/Test Engineer Role Overview Responsible for ensuring the quality, accuracy, and reliability of all data pipelines, model outputs, and business deliverables. Works across the full delivery lifecycle from data ingestion through to final business consumable outputs. Experience Required: 3–5 years Key Responsibilities Design and execute comprehensive test plans covering data pipelines, model outputs, and business reports Perform data quality validation — completeness, accuracy, consistency, and referential integrity checks across all data sources Execute source-to-target reconciliation and backfill validation across all ingestion runs Validate ML model outputs against known historical data and SME-reviewed samples Build and maintain automated test suites for post-refresh pipeline health checks Execute end-to-end integration and performance testing across the full pipeline Manage UAT in collaboration with business stakeholders — track, manage, and retest all defects Validate security, access controls, and audit-trail requirements across all data outputs Validate AI-generated narrative outputs for factual accuracy and consistency with underlying model scores Required Skills 3–5 years of experience in QA engineering, data testing, or software quality assurance Strong experience in data pipeline testing and data quality validation Proficiency in Python and SQL for test automation and reconciliation queries Experience with ML model output validation and testing approaches Familiarity with UAT management, defect tracking, and test case design Knowledge of cloud data platforms (Databricks, Snowflake) preferred Strong attention to detail with the ability to work across both technical and business workstreams QA/Test Engineer Role Overview Responsible for ensuring the quality, accuracy, and reliability of all data pipelines, model outputs, and business deliverables. Works across the full delivery lifecycle from data ingestion through to final business consumable outputs. Experience Required: 3–5 years Key Responsibilities Design and execute comprehensive test plans covering data pipelines, model outputs, and business reports Perform data quality validation — completeness, accuracy, consistency, and referential integrity checks across all data sources Execute source-to-target reconciliation and backfill validation across all ingestion runs Validate ML model outputs against known historical data and SME-reviewed samples Build and maintain automated test suites for post-refresh pipeline health checks Execute end-to-end integration and performance testing across the full pipeline Manage UAT in collaboration with business stakeholders — track, manage, and retest all defects Validate security, access controls, and audit-trail requirements across all data outputs Validate AI-generated narrative outputs for factual accuracy and consistency with underlying model scores Required Skills 3–5 years of experience in QA engineering, data testing, or software quality assurance Strong experience in data pipeline testing and data quality validation Proficiency in Python and SQL for test automation and reconciliation queries Experience with ML model output validation and testing approaches Familiarity with UAT management, defect tracking, and test case design Knowledge of cloud data platforms (Databricks, Snowflake) preferred Strong attention to detail with the ability to work across both technical and business workstreams