Based solely on the provided article, an LLM engineer's work centers on adapting, orchestrating, and serving pretrained large language models, differing from a general machine learning engineer. Demand for this role has grown substantially in 2026, as LLM features are now shipping as production systems. The roadmap covers five skill areas in order: foundations, prompting and tool calling, retrieval, fine-tuning and alignment, and serving and operations. The first step, building the foundation, requires a working-level understanding of tokens, embeddings, attention, and the transformer block, with PyTorch and the Hugging Face ecosystem as the default working environment. The second step involves designing prompts and building tool-calling systems, where prompting is described as the first lever an LLM engineer reaches for, requiring systematic thinking with structured system messages, few-shot examples, and JSON output schemas.