Senior Ml Engineer

This is production ML engineering at the senior level. You're getting deployment pipelines, MLOps tooling, model monitoring, and the full stack from PyTorch/TensorFlow to Kubernetes. It covers RAG systems, LLM integration, feature stores, and distributed processing with Spark and Kafka. The reference docs actually walk through production patterns, not just theory. Honest take: the performance targets (sub-100ms P95, 99.9% uptime) and the emphasis on monitoring, security, and cost optimization show this is aimed at real production systems, not notebooks. Use it when you need to move models from experiments to services that stay up.