Engineer

Job Description

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now. We are currently seeking a Engineer to join our team in Bangalore, Karn taka (IN-KA), India (IN). Key Responsibilities: Serve as the primary technical expert on DataRobot s AI platform, providing deep technical guidance and support. Collaborate with data scientists, AI engineers, and business stakeholders to implement and optimize DataRobot solutions. Troubleshoot and resolve complex issues related to DataRobot configuration, deployment, and monitoring. Onboard and configure existing SAS-based and DataRobot Prime v6 Python models into DataRobot v8.0.21. Ingest LST report files from SAS and upload metrics into DataRobot. Execute onboarded models with historical data and generate monitoring outputs within DataRobot. Compare current Power BI dashboard metrics with DataRobot outputs and develop equivalent visualizations within the platform. Lead the upgrade of DataRobot from version 8 to version 11 on Azure, working closely with technology partners. Understand and configure necessary Azure services (e.g., AKS, storage, networking) to support DataRobot deployment. Debug and resolve AKS container setup/configuration issues and propose scalable solutions. Provide training and support to internal teams on DataRobot functionalities and best practices. Develop and maintain comprehensive documentation including user guides, technical manuals, and best practice guidelines. Stay current with advancements in AI/ML and integrate relevant innovations into DataRobot solutions. Work through the Shield process and manage Jira stories for PreDev, PostDev, Prod, and RTx environments. Required Skills & Qualifications: Proven experience with DataRobot platform, especially in enterprise-scale deployments. Strong understanding of Azure cloud services, including AKS, networking, and storage. Hands-on experience with containerized deployments and Kubernetes troubleshooting. Proficiency in Python and familiarity with SAS model structures. Experience with model monitoring, performance metrics, and visualization tools like Power BI. Excellent problem-solving skills and ability to work independently in a fast-paced environment. Strong communication skills for cross-functional collaboration and training delivery. Familiarity with enterprise deployment processes, including Jira-based workflow management. Preferred Qualifications: Experience working in regulated environments such as banking or finance. Knowledge of U.S. Bank s Shield process or similar enterprise governance frameworks. Prior involvement in AI/ML model lifecycle management and MLOps practices