ML Engineer vs. AI Engineer
Since 2023, "AI Engineer" has mostly meant building products on top of existing LLMs through APIs: prompts, RAG, agents and evals. An ML Engineer traditionally trains and deploys models themselves, from data and features through to serving. There's overlap, but a strong AI Engineer may never have trained a model, and a strong ML Engineer may never have shipped an LLM feature.
How to tell them apart on a resume
ML Engineer
Training pipelines, feature engineering, PyTorch or TensorFlow, model accuracy metrics, MLflow, GPUs, recommendation or fraud models.
AI Engineer
OpenAI or Anthropic APIs, RAG, vector databases, LangChain, agents, prompt engineering, evals, often with a product or full-stack background.
The question that settles it
“Did you train the models yourself, or build products on top of models someone else trained?”
Read the full definitions
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