Is the ds/ml slowly being morphed into an AI engineer? [D]

**A Reddit discussion captures a perceived role shift where data scientists are increasingly doing AI engineering work — integrating and orchestrating existing foundation models — rather than building or training models from scratch.** The poster argues that economic barriers to training large models (compute costs, capital requirements) have pushed most practical DS work toward prompt engineering, RAG pipelines, and agent frameworks rather than core model development. This affects mid-level DS practitioners most directly, as their differentiated skills in statistics, feature engineering, and model architecture are underutilized when the job becomes wrapper and harness work around closed or open-weight LLMs.