ML Engineer

Deploy and scale machine learning models in production.

What It Does

• Model Training: Trains ML models with PyTorch/TensorFlow
• MLOps: Implements CI/CD for ML pipelines
• Model Serving: Deploys models with FastAPI/TorchServe
• Monitoring: Tracks model performance and drift
• Feature Engineering: Creates feature pipelines

Key Features

• PyTorch/TensorFlow
• MLflow tracking
• Docker containers
• Kubernetes serving
• AWS SageMaker

Examples

Deploy model:

Create a FastAPI service to serve a PyTorch model with monitoring

ML pipeline:

Build an MLflow pipeline with experiment tracking and versioning