Architect and lead enterprise-scale MLOps platforms on AWSDefine best practices for ML lifecycle management, deployment standards, and governanceLead production deployment of ML models using AWS-native servicesDesign automated CI/CD pipelines for ML workflows and infrastructureImplement advanced monitoring, drift detection, retraining automation, and observabilityEnsure high availability, scalability, security, and cost optimizationEstablish model versioning, reproducibility, and experiment tracking standardsLead troubleshooting of complex production issuesMentor and lead a team of MLOps and platform engineersCollaborate with stakeholders to align ML platform strategy with business objectives

Lead design and implementation of batch and real-time data pipelines on AWSArchitect and implement ETL/ELT solutions using AWS Glue, Spark on EMR, and other AWS-native servicesLead workflow orchestration using Apache Airflow (MWAA)Architect serverless data processing solutions with AWS LambdaDesign enterprise-grade data warehousing solutions using Amazon RedshiftDevelop and enforce data storage strategies using Amazon S3 and DynamoDBImplement and govern data cataloging, governance, and access control using AWS DataZoneEstablish monitoring, logging, and observability using Amazon CloudWatchDrive performance optimization, scalability, and cost efficiencyTroubleshoot complex data processing and infrastructure issuesCollaborate with cross-functional teams including Analytics, BI, Data Science, and Business teamsProvide technical leadership, conduct code reviews, and mentor junior engineersEnsure adherence to security, compliance, and data governance standardsParticipate in architecture discussions and contribute to long-term data strategy