Q: AI Engineer vs ML Engineer — what's the difference?

Think physics vs civil engineering. Both use the same laws of nature. Completely different professions.

ML engineers and data scientists work on the science side: building neural networks, training models, tuning them, running experiments, testing hypotheses. Their work is investigative, analytical, and experimental.

AI Engineers work on the building side. They don't actually build the AI. You don't need to build neural networks. The "brain" is a frontier model like GPT, Claude, or Gemini, running on the servers of OpenAI, Anthropic, or Google. You access it through API calls.

Your job is to build everything around that brain (this layer is called the harness): RAG pipelines, tool calling, embeddings, vector databases, agent orchestration, and deploying all of it to production securely and at reasonable cost.

One nuance worth knowing: many job postings titled "ML Engineer" are actually LLM-integration roles. Read the description, not the title. If it says RAG, agents, or LLM APIs, that's AI engineering work regardless of what the company calls it.