AI/ML Architect
Jobs via Dice
About the role
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Bizoforce Inc, is seeking the following. Apply via Dice today!
Role: AI/ML Architect
Location: Philadelphia, PA 19129
Duration: Long-Term
Description:
Key responsibilities: • Include AIML architecture and platform design 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.
Designer 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
Responsibilities
- Include AIML architecture and platform design 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
- Designer 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
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