Design, build, and maintain ML infrastructure and tooling to support the full ML lifecycle (data preparation, training, evaluation, deployment, monitoring, and retraining).
continuously improving, monitoring, maintaining, and extending the individual elements of the Data Ecosystem infrastructure in the cloud, e.g., cloud storage, file event queues, and databases, etc.
Design, develop and maintain AI-powered software tools that support engineers in their daily development workflows, from coding and code analysis to testing, documentation and technical problem solving
Own query performance and latency across Aurora MySQL and PostgreSQL through execution-plan analysis, indexing strategies, and database statistics optimization.