We are seeking a 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.
Responsibilities:
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Utilize Automatic Prompt Generation (APG) tools to create baseline prompts for complex parent-child template clusters.
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Run and supervise Automated Prompt Optimization (APO) tool, review the outputs, and flag when the APO reaches deadlocks or plateaus.
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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.
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Monitor shadowbot runs to ensure sufficient disagreements (between human and LLM ratings) are registered, generated, and tracked.
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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.
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Draft technical launch readiness justifications (Launch Certification Documentation) for final.
Requirement:
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Language Skills: Native fluency in Portuguese and fluent in English.
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Location: Must be based in Portugal.
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Education: Bachelor’s, Master’s, or Doctorate degree in Computer Science, Data Science, Computational Linguistics, Human-Computer Interaction (HCI), Cognitive Science, or a related analytical field.
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Prompt Engineering & AI Expertise: At least 4 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.
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Data Analysis: Strong proficiency in identifying error patterns, analyzing model performance, and using SQL or other data analytics tools.
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Technical Agility: Ability to quickly learn and master proprietary tools with minimal supervision.
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Communication: Excellent verbal and written communication skills.
Optional / Preferred Skills:
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Familiarity with enterprise-grade LLM interfaces like the Goose API.
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Experience in AI model evaluation, data science, computational linguistics, or software engineering.
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Hands-on experience with Automated Prompt Optimization (APO) systems or tuning workflows.
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Linguistic expertise, including an understanding of semantics and logic.