• Build and operationalize the infrastructure that allows machine learning to run reliably in production. • Architect and implement Built’s foundational ML Ops platform from scratch • Define and deploy reusable patterns for model training, deployment, monitoring, and retraining • Build CI/CD pipelines for ML lifecycle automation, including versioning and experimentation tracking • Stand up a feature store integrated with Snowflake and AWS to support structured and unstructured data • Implement model registry and governance standards to ensure reproducibility, auditability, and rollback capability • Integrate ML workloads into our event-driven architecture (Kafka, Kinesis) • Develop observability frameworks to monitor drift, performance, latency, and model quality in production • Automate ML infrastructure using Terraform and AWS-native tooling (SageMaker, Lambda, ECS, Batch, Step Functions) • Establish security and compliance standards across ML assets, including data lineage and access control • Mentor engineers on ML Ops patterns and deployment best practices