Sr Machine Learning Engineer

Machine Learning Engineer / Architect 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 o Monitor Data Quality - Monitor drift in data quality. o Monitor Model Quality - Monitor drift in model quality metrics, such as accuracy. o Monitor Bias Drift for Models in Production - Monitor bias in model's predictions. o 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.