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

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

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

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