We are currently seeking an experienced professional to join our team in the role AI- Led Data- Quality & Knowledge Management/Senior Associate Director

In this role, you will:

- Own the strategy and execution for AI-led data quality management across Service Management data domains including CMDB, incidents, problems, changes, service maps, knowledge articles, ownership data, control attributes, and operational metadata.
- Lead the design of a Service Management knowledge foundation enabling better context engineering, GenAI responses, service relationship discovery, impact analysis, and operational decision-making.
- Drive data quality governance by defining standards, controls, scorecards, ownership models, exception management, and remediation workflows.
- Partner with ServiceNow, CMDB, ITSM, Observability, Service Owners, Platform Engineering, and AI teams to improve data completeness, accuracy, consistency, timeliness, and lineage.
- Build and oversee AI data stewardship workflows, including automated recommendations for service relationships, CI enrichment, knowledge gaps, duplicate detection, stale data identification, and ownership validation.
- Translate business and operational pain points into data-driven and AI-enabled improvement opportunities.
- Establish lightweight engineering and automation capability for data profiling, validation, dashboards, exception queues, and continuous monitoring.
- Create executive-level reporting on data health,



knowledge maturity, AI-readiness, control gaps, and remediation progress; ensuring practices support audit, risk, regulatory, resilience, and operational control requirements.

To be successful in this role, you should meet the following requirements:

- Robust understanding of ITSM, CMDB, ServiceNow, service mapping, knowledge management, incident/problem/change processes, and operational data models.
- Ability to connect technical data quality issues with business outcomes (e.g., service availability, operational risk, faster RCA, better change decisions, improved AI accuracy).
- Working knowledge of GenAI, context engineering, knowledge graphs, data stewardship, metadata management, and agentic AI workflows.
- Experience with data governance, data quality frameworks, control design, dashboards, process automation, and cross-functional remediation.
- Ability to engage senior stakeholders and convert ambiguous operational issues into structured transformation plans.
- Proven capability to define and implement standards, controls, scorecards, and ownership models for data quality and knowledge maturity.
- Experience building monitoring and reporting mechanisms (dashboards, exception queues, continuous monitoring) for operational data health.
- Strong understanding of audit/risk/regulatory expectations as they relate to operational data, resilience, and controls.

📌 AI - Quality & Knowledge Management (Telangana)
🏢 hackajob
📍 Telangana