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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.
