Machine Learning Platform Engineer

Design, build, and operate scalable ML platform components including training infrastructure, feature stores, model registries, and inference services. Develop cloud-native, distributed systems and CI/CD pipelines to enable reliable, reproducible model deployments. Mature MLOps capabilities such as experiment tracking, data and model versioning, monitoring, and automated retraining. Establish best practices for model lifecycle management and deployment across development, staging, and production environments. Implement observability into ML systems to monitor performance, drift, data quality, and inference reliability; ensure security and cost-efficiency with governance partners. Collaborate with data scientists, ML engineers, and security teams to deliver reusable, self-service platform capabilities and promote engineering excellence.