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Detailed Job Description:

Experience:

7+ years experience in designing and developing enterprise class AI Platforms and solutions 3+ years of experience with enterprise fully automated Model and Risk management solution. 3+ years implementing Data ops, ML ops MS or BS in Computer Science, Information Science, Engineering or other related field Skills Deep understanding and hands on experience with ML Engineering techniques and tools including hands on experience with ML Operations. Experience with the primary managed data services within Google Cloud Platform, including AI Vertex, Cloud Bigtable, Cloud Spanner, Cloud SQL, or BigQuery Proficient in Data Science workbenches such as Domino, Container platform such as K8s/Docker, Core Java, J2EE, JSP, Servlet, Node.js, Angular, Proficient in Big Data Technologies, Data Transport (Pulsar/Kafka), Spark, Jupyter/ Python. Experience working with multiple databases: Cassandra, PostGreS, Teradata. and NoSQL and RDBMS Technologies Container platform such as K8s/Docker, Experience with various agile methodologies and tools: JIRA, Confluence, Gitlab, CICD, etc. Exposure to product based development methodology is desirable Strong leadership, communication, persuasion and teamwork skills

ML Model Management Platform Strategy:

Define and Architect comprehensive Model Management framework across these 4 major areas

Monitor Data Quality - Monitor drift in data quality. Monitor Model Quality - Monitor drift in model quality metrics, such as accuracy. Monitor Bias Drift for Models in Production - Monitor bias in models predictions. Monitor Feature Attribution Drift for Models in Production - Monitor drift in feature attribution.

Technology / Execution:

Build and implement a platform for Seamless integration and interface with existing Batch and Realtime ML systems to enable track performance metrics and verify the accuracy of predictions Design/Implement a clean UI so that Data drift, model quality, and other health statistics are provided in an easy-to-understand interface to enable quick assessment of the business impact and initiate proactive actions Implement appropriate notifications, alerts for both upstream and downstream systems.