AI Strategist

This role bridges enterprise data architecture and applied AI, defining how AI/ML technologies can automate metadata discovery, lineage analysis, and data-modernization planning.
Key Responsibilities

• Develop and execute an AI/ML strategy for automating metadata ingestion, lineage inference, and anomaly detection.
• Identify and evaluate opportunities to embed Generative AI and predictive analytics into existing data-engineering processes.
• Collaborate with data-engineering and architecture teams to integrate AI solutions within AWS and Azure ecosystems.
• Assess, prototype, and recommend tools such as Azure AI Services , AWS SageMaker , Databricks ML , and OpenAI APIs .
• Define long-term modernization and automation roadmaps; produce clear documentation and stakeholder presentations.
• Establish governance and model-lifecycle best practices for AI components integrated into the data platform.
  
  Required Skills & Experience
• 10-15 years overall experience, with 3-5 years in AI/ML strategy or enterprise data-modernization leadership.
• Proven ability to design and implement AI-driven architectures for data-management or metadata initiatives.
• Strong understanding of metadata management , data-lineage frameworks , and data-governance principles .
• Hands-on familiarity with cloud AI platforms : Azure AI, AWS SageMaker, Databricks ML Flow, or comparable tools.
• Experience defining AI roadmaps, proof-of-concepts, and ROI frameworks for large organizations.
• Excellent stakeholder communication, executive reporting, and technical-writing skills.
  Nice to Have
• Consulting background in AI advisory , data strategy , or digital-transformation programs.
• Exposure to GenAI use cases for data quality, lineage inference, or ETL optimization.
• Understanding of data-modernization tools (BladeBridge, Informatica IDMC, Collibra, Alation).