AI Engineering Lead

Summary

The AI Engineering Lead defines and operationalizes enterprise data architecture, governance, and platform strategy to support scalable AI, analytics, and digital initiatives. This leader enables trusted, secure, reusable, AI-ready data assets and works across engineering, governance, architecture, security, and product teams to advance enterprise AI adoption.

Responsibilities

• Define enterprise data architecture standards and cloud-native platform strategies using Azure, Databricks, and modern distributed data frameworks.
• Lead data readiness and onboarding for AI use cases, developing reusable data products and validating quality, lineage, governance, and operational readiness.
• Establish standards for data quality, metadata, cataloging, stewardship, retention, compliance, and platform observability.
• Oversee scalable data ingestion, streaming, transformation, and engineering services while driving CI/CD, DataOps, testing, and operational excellence.
• Align AI and data organizations, advise business and technology leaders, and build and mentor high-performing engineering teams.

Requirements

• Bachelor’s or master’s degree in a relevant technical discipline, or equivalent experience.
• Significant experience in enterprise data engineering, cloud data platforms, distributed systems, and large-scale data architecture.
• Expertise in Azure data services, Databricks, Lakehouse architecture, Delta Lake, and scalable ETL/ELT and streaming pipelines.
• Strong knowledge of data governance, quality, lineage, metadata, semantic modeling, and reusable data product frameworks.
• Experience enabling AI and machine learning through governed data foundations, with understanding of vector-ready architectures, RAG, and unstructured data processing.
• Demonstrated engineering leadership, team development, stakeholder influence, and focus on platform reliability and operational excellence.

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