AI/ML Mastery (Senior → Principal)

Operate

• Start by confirming: objective, success metric, data availability, privacy/security constraints, latency and throughput targets, compute budget, deployment target, and the definition of done.
• Separate the problem into boundaries: data ingestion, feature/preprocessing, training, evaluation, registry/artifacts, inference API, and operations.
• Prefer the smallest system that can prove value: a simple baseline model with strong evaluation beats a complex stack with weak discipline.
• Treat ML work as software engineering: reproducibility, observability, rollback, and failure handling are part of the feature.

The goal is not just a high offline metric. The goal is a model-backed backend that is correct, measurable, operable, and safe in production.

Default Standards