Gen AI - Data Modeling

Key Responsibilities

Client Advisory & Business Development

• Partner with client stakeholders and executives to understand business challenges and translate them into scalable AI and data-driven solutions.
• Identify opportunities to expand engagements and drive account growth through strategic consulting.
• Lead technical discussions, develop proposals, and present architecture roadmaps to clients.

Architecture & Data Modeling

• Define and own reference architectures for enterprise GenAI and RAG platforms, along with supporting data ecosystems.
• Lead end-to-end data modeling initiatives, including designing and enhancing the Silver layer within a Medallion architecture to ensure clean, standardized, and AI-ready data.
• Establish comprehensive data lineage across ingestion, transformation, vectorization, prompt execution, and AI outputs.

Data Quality & Governance Strategy

• Develop enterprise-wide data governance standards, policies, and quality frameworks for AI systems.
• Design mechanisms to detect data drift, hallucinations, and low-confidence outputs, ensuring systems respond safely or restrict outputs when trust thresholds are not met.
• Align regulatory requirements with technical governance controls.

Team Leadership & Mentorship

• Provide architectural guidance and mentorship to technical leads, senior engineers, and data teams.
• Collaborate with security, platform, and data engineering teams for seamless solution delivery.
• Establish best practices for MLOps, CI/CD pipelines, and observability in production-grade GenAI systems.

Required Skills & Experience

• 12–15+ years of experience, including a strong track record as a Data, AI, or Solution Architect.
• Deep expertise in data modeling and data engineering, especially within Medallion architectures (Bronze, Silver, Gold) and modern data warehousing.
• Strong consulting and client engagement experience, including pre-sales and solution development.
• Proven ability to design and implement production-grade GenAI and RAG architectures.
• Advanced knowledge of AI data governance, lineage, and data quality frameworks.
• Hands-on experience with cloud platforms such as Azure, AWS, or Google Cloud Platform, including Azure AI services.
• Strong background in API design, distributed systems, MLOps, and observability.