Title: Machine Learning Architect Location: Basking Ridge, NJ OR Rochelle Park, NJ (Hybrid) Duration: 6-12+ Months w2 Hybrid: 3 to 5 days a week in the office (Basking Ridge, NJ or Rochelle Park, NJ) 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, Client ops MS or BS in Computer Science, Information Science, Engineering or other related field Skills: Deep understanding and hands on experience with Client Engineering techniques and tools including hands on experience with Client 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 Client 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 Client 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.