at Capgemini
ConsultanciesPosted 13 days ago
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**DB Tester: Test Data Accuracy, Model Bias; Master Japaneese** - **Design**, build & maintain automated test frameworks for data pipelines, transformations, and AI/ML models. - **Define** and govern evaluation datasets, quality benchmarks; **drive** CI/CD, DevOps, MLOps adoption. - **Execute** test strategies, **validate** data accuracy, model bias/fairness, regulatory compliance; **report** key quality metrics. - **Proactively** partner with cross-functional teams, **ensure** alignment with requirements; **enhance** data governance & security practices. - **Fluent in Japanese**, with a commitment to staying current with AI technologies & industry QA standards. - **Role requires** experience in test automation, data QA, and a proven track record in improving data quality.
- Compensation
- Not specified
- City
- Hyderabad
- Country
- India
Currency: Not specified
Full 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:Ø Data quality indicators (e.g., freshness, validity, reconciliation, SLA adherence)Ø Evaluation coverage (datasets, scenarios, edge cases)Ø Model performance (task-specific KPIs, drift)Ø Hallucination and bias rates (where applicable)Ø 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:Ø Data quality indicators (e.g., freshness, validity, reconciliation, SLA adherence)Ø Evaluation coverage (datasets, scenarios, edge cases)Ø Model performance (task-specific KPIs, drift)Ø Hallucination and bias rates (where applicable)Ø 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.
