Prompt Engineer resume example

A complete example below. The person is invented; the writing is not. The notes on the right point at what's worth copying — swap in your own names, companies and numbers.

Jordan Lin
Prompt Engineer
(555) 010-0000jordan.lin@example.comSeattle, WA
Summary
Three years in NLP and LLM applications, focused on turning unstable model output into something shippable. Worked across support, moderation and structured extraction. I build the evaluation set before touching the prompt, so every improvement comes with a baseline and a cost.
Education
2018 - 2022State University
B.S. Software Engineering
Coursework in machine learning and NLP; capstone on short-text intent classification.
Experience
Mar 2023 - PresentAI Startup
Prompt Engineer
Own answer quality for an AI support agent; built a 480-item evaluation set covering long-tail cases and raised human pass rate from 68% to 91%
Moved from free text to enforced structured output with a validation fallback, dropping downstream parse failures from 7.2% to 0.4%
Designed tiered routing — small model for simple intents, escalation only when needed — cutting average cost per call 55% and P95 latency by 1.8s
Set up a model-upgrade regression process so effectiveness drops surface before release, not after
Jul 2022 - Feb 2023Consumer Internet Company
NLP Engineer
Iterated a review sentiment classifier from 0.81 to 0.88 F1
Owned training-data cleaning and the labelling guidelines; inter-annotator agreement rose from 0.72 to 0.89
Projects
Jan 2024 - Aug 2024Content Safety Guardrail
Sole owner
A guardrail layer that blocks non-compliant generations and returns an explainable reason.
Combined few-shot classification with rule pre-filters: miss rate from 4.1% to 0.9% while holding false blocks under 1.2%
Shipped as an independently rollable and revertible layer, decoupled from the main path
Skills
Evaluation set construction and regression testing; every change quantified against a baseline
Structured output, few-shot and chain-of-thought techniques — and where each stops working
Token cost and latency modelling; caching and tiered routing
Python; comfortable building batch evaluation harnesses against major model APIs

Three mistakes this example avoids

  • Pasting prompt text into the resume instead of describing the outcome
  • No baseline — 'improved output quality' with nothing to compare against
  • Ignoring cost and latency, which is what actually blocks rollout

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