machine-learning-engineer

SKILL.md

ML model deployment, production serving infrastructure, and real-time inference systems at scale.

Handles model optimization (quantization, pruning, distillation), serving APIs (REST/gRPC), and container orchestration with auto-scaling on Kubernetes or cloud platforms

Supports real-time inference, batch prediction systems, multi-model serving with intelligent routing, and A/B testing for model comparisons

Covers edge deployment for IoT and mobile with model compression, offline capability, and resource-constrained optimization

Implements monitoring, health checks, graceful degradation, circuit breaking, and observability for production reliability

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.