A complete, annotated resume for a prompt engineer who transitioned from content strategy. Every section is broken down — from quantifying LLM work to framing a non-traditional background as a strength.
Scroll down to see the full resume, then read why each section works.
Prompt engineer with 2 years of experience designing and optimizing LLM systems for production applications. Currently at Scale AI, where I improved task accuracy from 67% to 94% on a document extraction pipeline and reduced token costs by 41% across 3 enterprise deployments. Background in computational linguistics and content strategy, with self-taught Python and a focus on evaluation-driven prompt development.
Prompt Engineering: Chain-of-thought, few-shot design, system prompting, output structuring, RAG LLM Platforms: Claude API, OpenAI API, LangChain, LlamaIndex, Weights & Biases Evaluation: Custom eval frameworks, A/B testing, hallucination detection, human-in-the-loop review Languages: Python, SQL, JavaScript Domain: NLP, information retrieval, content taxonomy, computational linguistics
Seven things this prompt engineer resume does that most AI resumes don’t.
Taylor went from content strategist at HubSpot to AI content strategist at Jasper to prompt engineer at Scale AI. The resume doesn’t hide this trajectory — it makes it a narrative arc. The HubSpot role shows linguistic foundations (content taxonomy, style guidelines). Jasper shows the pivot moment (writing prompt templates for 100K+ users). Scale AI shows full technical depth. Each role builds on the last, and nothing is wasted.
Accuracy improvements (67% to 94%), token cost reduction (41%), hallucination rates (18% to 3.5%), relevance scores (0.72 to 0.91). These aren’t vague claims about “improving AI outputs” — they’re precise engineering metrics. The biggest mistake prompt engineer resumes make is treating the work as creative writing. Taylor treats it as systems engineering with measurable outcomes, and that’s what gets interviews.
“AI/ML enthusiast with experience in generative AI” tells a hiring manager nothing. “Claude API, LangChain, LlamaIndex, custom eval frameworks, hallucination detection” tells them exactly what Taylor can do on day one. The skills are categorized into Prompt Engineering, LLM Platforms, Evaluation, Languages, and Domain — mirroring how AI companies actually structure their job postings.
Building a nightly regression testing pipeline with 1,200+ test cases is not “prompt writing” — it’s software engineering applied to LLM systems. This single bullet does more to establish technical credibility than any certification or course completion could. It signals that Taylor doesn’t just write prompts; they build the infrastructure to ensure those prompts work reliably at scale.
A linguistics major who built an open-source Python library with 480+ GitHub stars and adoption by YC startups? That’s a stronger signal than any bootcamp certificate. The project (prompteval) is directly relevant to prompt engineering work and demonstrates that Taylor can write production Python, design APIs, and build tools that other engineers find useful enough to adopt.
“Designed the RAG pipeline” with “hybrid retrieval (BM25 + vector search) with re-ranking” is the kind of architectural decision that separates prompt engineers from prompt writers. Taylor isn’t just crafting text — they’re designing retrieval architectures, choosing between search strategies, and measuring the results with benchmark sets. That’s the trajectory of this role, and the resume demonstrates it clearly.
The education section is at the bottom, just like it should be. But the “computational linguistics” callout in the summary ties the degree directly to the work. Linguistics is the study of how language works structurally — which is exactly what prompt engineering requires. Taylor doesn’t downplay the degree or over-explain it. It’s just there, quietly reinforcing the narrative that this person understands language at a deeper level than most engineers do.
The weak version describes what anyone with ChatGPT access could claim. The strong version describes a specific system, a measurable improvement, and a technical approach. That’s the difference between a prompt enthusiast and a prompt engineer.
The weak version apologizes for the career change and leads with personality. The strong version leads with the current role, specific results, and production-level scope — nobody cares that you’re “excited,” they care that you can ship.
The weak version mixes vague categories (“AI”) with soft skills (“Critical Thinking”) and consumer products (“ChatGPT”). The strong version lists production tools, specific techniques, and frameworks that a hiring manager can match against the job description in seconds.
This exact resume template helped our founder land a remote data scientist role — beating 2,000+ other applicants, with zero connections and zero referrals. Just a great resume, tailored to the job.
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