MLOps Platform Engineer - Enterprise Infrastructure

About the opportunity

Build unified ML training, evaluation, and edge quantization pipelines powering on-device intelligence across iOS & macOS.

What you will do

• check_circle Architect automated continuous integration and continuous training (CI/CD/CT) platforms for machine learning.
• check_circle Build model registries, feature stores, and automated regression testing suites for on-device CoreML models.
• check_circle Optimize distributed model evaluation benchmarks running across multi-cloud Kubernetes infrastructure.
• check_circle Monitor model drift and performance telemetry adhering to privacy-preserving differential privacy standards.

What we are looking for

• arrow_circle_right 5+ years experience building production MLOps and infrastructure tooling in cloud and edge settings.
• arrow_circle_right Deep expertise in Kubernetes, Kubeflow, MLflow, Docker, Python, and Go.
• arrow_circle_right Solid understanding of model quantization (FP16, INT8, INT4) and hardware-aware neural architecture search.

Skills & Tech Stack