• Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads. • Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling. • Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets. • Monitor, test, and improve data quality, model performance, latency, and cost. • Partner with product, data science, and security teams to ship resilient, compliant services. • Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices. • Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions. • Embrace Twilio’s “We are Builders” values by taking ownership of problems and driving them to completion.