ml-engineer

Production model serving with PyTorch and TensorFlow is configured using TorchServe, TensorFlow Serving, and BentoML, orchestrated through Docker and Kubernetes to ensure reliable API endpoints for inference.

Feature engineering and feature stores are implemented to manage and serve features consistently across batch and streaming contexts, ensuring reliable data pipelines for training and real-time inference workflows.

A/B testing infrastructure for deployed models works by routing live traffic between different model versions, validating reliability and performance metrics across batches before promoting a challenger model.

Yes, Ray integrates with Hugging Face and PyTorch 2.x to execute distributed training and hyperparameter search, scaling transformer workloads efficiently across cloud-native environments.

Model monitoring requires drift detection and CI/CD in MLOps to automate retraining pipelines, identify data shifts across streaming contexts, and maintain production stability without manual intervention.

Yes, the MLOps workflow supports cost optimization for Kubernetes and Docker deployment stacks by validating resource allocation, maintaining observability, and automating reliability checks across the model lifecycle.