DB Tester

Capgemini; Navi Mumbai; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Capgemini India in Navi Mumbai seeks a QA Engineer to design, build, and maintain automated test frameworks for data integration, AI/ML models, and data pipelines. You will define evaluation datasets, quality benchmarks, and comprehensive test strategies.

You will collaborate with Data Engineering, App Dev, Platform, Security, and Risk teams to monitor data quality, model performance, and compliance, while advancing CI/CD, MLOps, and test automation practices.

Qualifications

• Design, build, and maintain automated test frameworks for data pipelines and AI/ML models.
• Define evaluation datasets and quality benchmarks for data and AI systems.
• Develop test strategies to validate data accuracy, completeness, consistency, lineage, and model bias.
• Collaborate with cross-functional teams to align QA with requirements and acceptance criteria.

Responsibilities

• Design, build, and maintain automated test frameworks for data integration pipelines, transformation processes and AI/ML models.
• Define and govern evaluation datasets, golden data, and quality benchmarks for data and AI systems.
• Develop and execute comprehensive test strategies to validate data accuracy, completeness, consistency, lineage, model bias/fairness, and regulatory compliance.
• Build and automate end-to-end evaluation pipelines for large-scale data and AI workflows (batch/streaming where applicable).
• Partner with cross-functional teams to define QA strategy and ensure alignment with requirements and acceptance criteria; monitor quality metrics such as data freshness, validity, reconciliation, and SLA adherence.

Skills

Japanese

Job description

Job Description:

Your Role

The successful candidate will be responsible for the following: - Must know Japaneese

• Design, build, and maintain automated test frameworks for data integration pipelines, transformation processes and AI/ML models.
• Define and govern evaluation datasets, golden data, and quality benchmarks for data and AI systems.
• Develop and execute comprehensive test strategies to validate data accuracy, completeness, consistency, lineage, model bias/fairness, and regulatory compliance.
• Build and automate end-to-end evaluation pipelines for large-scale data and AI workflows (batch/streaming where applicable).
• Partner with cross-functional teams (Data Engineering, App Dev, Platform, Security, Risk/Compliance, and Business) to define QA strategy and ensure alignment with requirements and acceptance criteria. Monitor, analyze, and report key quality metrics, including: Data quality indicators (e.g., freshness, validity, reconciliation, SLA adherence) Evaluation coverage (datasets, scenarios, edge cases) Model performance (task-specific KPIs, drift) Hallucination and bias rates (where applicable) Compliance incident rates and audit findings
• Implement advanced QA capabilities such as anomaly detection, automated data validation, model behavior testing, and robustness/guardrail evaluations.
• Ensure adherence to data governance, privacy, and security standards (access controls, PII handling, retention, auditability).
• Drive adoption of best practices across CI/CD, DevOps, MLOps, and test automation (quality gates, versioning, reproducibility).
• Stay current with evolving AI technologies, regulatory expectations, and industry QA standards; translate them into actionable engineering practices.
• Champion a culture of quality, innovation, and continuous improvement through standards, playbooks, and coaching across engineering teams.

Your Profile

The successful candidate will be responsible for the following: - Must know Japaneese

• Design, build, and maintain automated test frameworks for data integration pipelines, transformation processes and AI/ML models.
• Define and govern evaluation datasets, golden data, and quality benchmarks for data and AI systems.
• Develop and execute comprehensive test strategies to validate data accuracy, completeness, consistency, lineage, model bias/fairness, and regulatory compliance.
• Build and automate end-to-end evaluation pipelines for large-scale data and AI workflows (batch/streaming where applicable).
• Partner with cross-functional teams (Data Engineering, App Dev, Platform, Security, Risk/Compliance, and Business) to define QA strategy and ensure alignment with requirements and acceptance criteria. Monitor, analyze, and report key quality metrics, including: Data quality indicators (e.g., freshness, validity, reconciliation, SLA adherence) Evaluation coverage (datasets, scenarios, edge cases) Model performance (task-specific KPIs, drift) Hallucination and bias rates (where applicable) Compliance incident rates and audit findings
• Implement advanced QA capabilities such as anomaly detection, automated data validation, model behavior testing, and robustness/guardrail evaluations.
• Ensure adherence to data governance, privacy, and security standards (access controls, PII handling, retention, auditability).
• Drive adoption of best practices across CI/CD, DevOps, MLOps, and test automation (quality gates, versioning, reproducibility).
• Stay current with evolving AI technologies, regulatory expectations, and industry QA standards; translate them into actionable engineering practices.
• Champion a culture of quality, innovation, and continuous improvement through standards, playbooks, and coaching across engineering teams.

For this role, the gross annual starting base salary is [X] [currency] (full-time). This covers base pay only; any bonuses, incentives, and benefits will be discussed later in the recruitment process. Candidates with additional experience or qualifications may receive a higher offer, determined by objective, gender-neutral criteria and consistent with our pay principles. If a collective labour agreement applies, we will explain the relevant pay terms at the interview stage. Note: We never ask for your current or previous salary during our hiring process.