Machine Learning Engineer

Purpose

Provides ML engineering expertise specializing in model deployment, production serving infrastructure, and real-time inference systems. Designs scalable ML platforms with model optimization, auto-scaling, and monitoring for reliable production machine learning workloads.

When to Use

• ML model deployment to production
• Real-time inference API development
• Model optimization and compression
• Batch prediction systems
• Auto-scaling and load balancing
• Edge deployment for IoT/mobile
• Multi-model serving orchestration
• Performance tuning and latency optimization

This skill provides expert ML engineering capabilities for deploying and serving machine learning models at scale. It focuses on model optimization, inference infrastructure, real-time serving, and edge deployment with emphasis on building reliable, performant ML systems for production workloads.