AI/ML Quality Engineering
Responsible for designing and executing quality assurance processes for AI/ML models, ensuring robustness, fairness, and compliance across production pipelines. Collaborate with data scientists and engineering teams to automate testing, monitor model drift, and integrate CI/CD workflows.
Skills required
About this role
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
Responsibilities
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
Qualifications
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
