AI Governance

Position: Responsible AI Governance &
Location: Noida Sector 27

Greeting from TCS!!

We are looking for  Data Quality & Responsible AI Control Operations

Role:
Specialist , Data Quality & Responsible AI Control Operations
Location
-Noida
Experience range  
-8+

Role Summary
The Specialist, Data Quality & Responsible AI Control Operations is a hands-on role responsible for implementing, maintaining, and improving data quality and AI data readiness controls across authorized data sources, data products, and AI use cases. This role supports the connection between data architecture, hands-on data modeling, data management, Responsible AI, and control operations to help ensure AI products consume governed, traceable, fit-for-purpose, and high-quality data.

The ideal candidate brings practical experience in data quality engineering, data modeling, data governance, metadata, lineage, control execution, and automation, with the ability to work closely with domain, technology, risk, compliance, audit, and AI governance stakeholders.

Key Responsibilities
Implement data quality and AI data readiness controls across authorized data sources, data products, semantic products, and AI use cases.
Support definition and application of AI-ready data criteria, including quality thresholds, lineage completeness, metadata completeness, source authorization, classification, access controls, issue history, freshness, and remediation expectations.
Translate Responsible AI control requirements into practical data control tasks, test cases, rule logic, evidence requirements, and implementation steps.
Partner with data architects, data engineering, platform, and domain teams to implement controls across ingestion, transformation, publication, semantic access, AI consumption, and runtime monitoring.
Perform hands-on data modeling across conceptual, logical, physical, canonical, and semantic models to support trusted data products, ADS certification, AI consumption patterns, and downstream DQ control design.
Configure and maintain DQ rules, thresholds, evidence payloads, control templates, metadata mappings, and implementation artifacts using approved patterns.
Work with domain teams to define DQ rules, set thresholds, emit raw DQ metrics, manage exceptions, remediate issues, and provide evidence in alignment with central governance expectations.
Execute recurring data profiling, rule runs, exception reviews, issue triage, root-cause analysis support, remediation tracking, retesting, recertification, and closure evidence.
Support integration of data quality controls into AI lifecycle gates so AI products use fit-for-purpose, authorized, governed, traceable, and appropriately controlled data sources.
Maintain control library entries for data quality, AI data readiness, metadata, lineage, access, privacy, monitoring, certification, and lifecycle governance.
Identify and implement automation opportunities that reduce manual governance effort while improving traceability, repeatability, defensibility, and audit readiness.
Build and maintain monitoring thresholds, alerts, KRIs, KPIs, control effectiveness measures, dashboards, and reports that show data quality health, AI data readiness, exceptions, and remediation progress.
Coordinate with business owners, product teams, data domains, platform engineering, architecture, security, privacy, legal, compliance, risk, model risk, and audit to support execution of control requirements.
Maintain audit-ready documentation, including control mappings, rule logic, test results, workflow decisions, approvals, exceptions, incident records, remediation evidence, and management reporting inputs.
Contribute to playbooks, standards, implementation guidance, training, and enablement materials that help business and technology teams adopt DQ and RAI control practices.
Required Skills and Experience
Practical experience in enterprise data quality, data governance, data management, data architecture, technology controls, Responsible AI operations, or a closely related discipline within a complex enterprise environment.
Good understanding of enterprise data architecture, hands-on data modeling, authorized data sources, data products, data contracts, metadata, lineage, semantic layers, access controls, and governed lakehouse or cloud data platform patterns.
Hands-on experience designing, reviewing, or maintaining conceptual, logical, physical, canonical, dimensional, domain, and semantic data models, including entity relationships, critical data elements, business definitions, data contracts, and AI-consumable semantic structures.
Hands-on knowledge of data quality practices, including rule design, profiling, thresholds, data observability, reconciliation, anomaly detection, issue management, remediation, and quality scorecards.
Ability to connect data quality outcomes to Responsible AI control needs, including traceability, data suitability, representativeness, bias/proxy-risk considerations, privacy constraints, monitoring, and lifecycle governance.
Experience implementing controls in pipelines,…