Senior Machine Learning Operations Engineer

You’ll partner closely with Data Engineering, Data Science, and Platform teams to establish how ML systems operate across Built. What You’ll Do: You’ll build and operationalize the infrastructure that allows machine learning to run reliably in production. Specifically, you will 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 This role is hands-on and foundational. You’ll be shaping how machine learning operates at Built for years to come. Skills & Experience We’re looking for a builder - someone who has personally designed and productionized ML infrastructure before.