AI Architect Lead

Define and govern practical AI and agentic capabilities that augment MDM/RDM delivery and data stewardship while maintaining strict controls around PHI/PII, explain ability, auditability, and human decision rights.

• Identify and prioritize AI use cases, including:
  • Mapping recommendations
  • Duplicate/orphan detection
  • Match explanations
  • Data Quality (DQ) anomaly detection
  • Impact analysis
  • Natural-language data discovery
• Define AI architecture, model selection, data access, prompt/context patterns, evaluation strategies, and MDM/RDM integration.
• Design human-in-the-loop (HITL) controls to ensure AI never changes authoritative master or reference data without approved governance.
• Define evidence, traceability, and explainability for AI-generated match, mapping, and DQ recommendations.
• Establish AI privacy and security controls for PHI/PII, including access, logging, retention, and data minimization.
• Define evaluation datasets, accuracy thresholds, drift monitoring, and production quality controls.
• Lead AI proof-of-concepts (POCs) and transition successful use cases into governed production services.

• AI use-case portfolio and prioritization
• AI reference architecture
• AI governance and guardrail specification
• Evaluation framework and benchmark results
• Pilot and production integration design
• AI monitoring and operating model

• 8+ years of experience in AI/ML architecture, including enterprise Generative AI and Agentic AI patterns.
• Strong data architecture background with experience integrating AI capabilities into enterprise data workflows.
• Experience deploying approved AI/ML models with human-in-the-loop governance and controls .
• Strong understanding of AI architecture, model evaluation, security, governance, and productionization.
• Experience working with enterprise data platforms and MDM/RDM environments.

• Experience with Responsible AI and regulated-data environments.
• Experience with Google Vertex AI or equivalent AI services on GCP.
• Experience with MLOps, model monitoring, drift detection, and production AI operations .
• Experience working with PHI/PII and enterprise data privacy requirements.