• Deploy and maintain containerized workloads to support machine learning development, deployment, and post-deployment monitoring. • Utilize tools like helm and kustomize to accelerate the deployment of machine learning models and data pipelines. • Apply various optimization techniques such as compilation, quantization-aware-training (QAT), and pruning to improve latency and throughput of models. • Utilize open-source software like Metaflow, Prefect, Temporal, and Argo Workflows to facilitate data science development. • Build and optimize machine learning models to analyze security data, extract actionable insights, and identify trends, anomalies, and other relevant security signals. • Develop and maintain systems for drift detection and model monitoring to ensure continuous improvement and accuracy of insights. • Collaborate with cross-functional teams to design data pipelines that can efficiently process petabytes of raw internet security data.