AI/ML Architect

AIML Architecture And Platform Design

Key responsibilities include AIML architecture and platform design to define the end to end AI/ML reference architecture from data injection through model serving and monitoring. Establish standards for data storage, access patterns and lineage, including separation of raw, curated and feature ready data. Assess and define the need for shared platform capabilities such as feature stores, model registries, AIML catalogues, experiment tracking, design for scale across multiple business units with differing data sensitivity, regulatory and operational needs.

Environment and delivery pipeline: Define standard development, validation and production environments for AIML workloads. Design a repeatable ML delivery pipeline covering model development and training, validation, approval and promotion, deployment (batch and/or real time), monitoring drift detection and retraining, establish CI/CD (and continuous training where appropriate) best practices for ML systems.