Develop and maintain data architecture: create and manage robust data architectures that support high-volume, high-throughput SaaS applications, focusing on reliability and scalability. * Strengthen data quality and model performance through well-designed ETL/ELT pipelines, streaming systems, feature store integration, and workflow orchestration. * Ensure reliable and trustworthy AI operations by implementing comprehensive observability: logs, metrics, traces, and model/data drift detection. * Reduce operational risk by embedding security and compliance best practices — IAM, RBAC, VPC design, secrets management, and encryption — into every layer of the stack. * You have previously owned end-to-end ML/AI infrastructure — from data ingestion and feature pipelines to tra
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