Last updated: September 2026
Senior LLM roles require proof of production model work — fine-tuning, inference optimization, eval, and guardrails — not tutorial completions. Use this format to show token economics and reliability at scale.
Single-column, reverse-chronological, 1–2 pages. Lead with metrics: latency (p95), tokens/query, model accuracy on eval sets, fine-tune lift, and cost savings. Keywords: LoRA/QLoRA, vLLM, Triton, Hugging Face, prompt routing, structured outputs, JSON mode, moderation, red-teaming, LangSmith tracing.
Related: AI professionals hub · Agentic AI engineer resume · LLM engineer fresher guide · Free ATS resume builder
Example bullet: "Deployed vLLM serving for a 7B fine-tuned support model, reducing p95 latency from 1.8s to 420ms and cutting monthly inference spend 31% via semantic caching."
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Check ScoreModel selection, fine-tuning, inference serving, prompt systems, safety, and quantified cost/latency/quality metrics — not just API wrappers.
LLM engineers center on models and inference; agentic engineers on multi-agent orchestration and tool calling. Blend both if the JD spans them.
Yes — with routing, caching, eval, fallbacks, and cost controls that show production maturity.
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