Project Lion - Lead Prompt Engineer - United States (Remote, Part-Time)

Welocalize

6d • Remote

About The Position

We are seeking a Lead Prompt Engineer to be responsible for the end-to-end technical migration workflow for transitioning templates to LLM autoraters. The role is required to use client’s internal tools to leverage prompt engineering techniques to maximize model performance.

Requirements

• Language Skills: Native fluency in English.
• Location: Must be based in United States.
• Education: Master’s, or Doctorate degree in Computer Science, Data Science, Computational Linguistics, Human-Computer Interaction (HCI), Cognitive Science, or a related analytical field.
• Prompt Engineering & AI Expertise: At least 7 years' experience as Prompt Engineer. Proven experience tuning Large Language Models (LLMs) for strict, structured outputs, complex classification tasks, and familiarity with chain-of-thought and few-shot learning.
• Data Analysis: Strong proficiency in identifying error patterns, analyzing model performance, and using SQL or other data analytics tools.
• Technical Agility: Ability to quickly learn and master proprietary tools with minimal supervision.
• Communication: Excellent verbal and written communication skills.

Nice To Haves

• Familiarity with enterprise-grade LLM interfaces like the Goose API.
• Experience in AI model evaluation, data science, computational linguistics, or software engineering.
• Hands-on experience with Automated Prompt Optimization (APO) systems or tuning workflows.
• Linguistic expertise, including an understanding of semantics and logic.

Responsibilities

• Utilize Automatic Prompt Generation (APG) tools to create baseline prompts for complex parent-child template clusters.
• Run and supervise Automated Prompt Optimization (APO) tool, review the outputs, and flag when the APO reaches deadlocks or plateaus.
• Manually draft, test, and refine prompts to navigate complex template architectures, overcome anti-patterns, and handle edge cases where tooling is lacking or broken. Solve edge-case scenarios by designing and refining manual prompts.
• Monitor shadowbot runs to ensure sufficient disagreements (between human and LLM ratings) are registered, generated, and tracked.
• Run prompt versions against established gold data to continuously measure autorater quality against the human crowd baseline, calculating accuracy metrics such as F1 scores, precision, and recall.
• Draft technical launch readiness justifications (Launch Certification Documentation) for final.

What This Job Offers

• Job Type: Part-time
• Career Level: Mid Level
• Number of Employees: 1,001-5,000 employees