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Welo Global

Project Lion – Senior Prompt Engineer – Portugal (Remote, Part-Time)

AI Summary
This senior prompt-engineering contract role leads the technical migration of template workflows to LLM autoraters. The specialist will use automated prompt generation and optimization tools, manually refine prompts for complex edge cases, and monitor model-versus-human evaluation outcomes. Core work includes validating autorater quality against gold data using F1, precision, and recall, then preparing launch-readiness documentation. The position requires native Portuguese, fluent English, Portugal residency, and at least four years of relevant prompt-engineering experience.
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Remote from
Portugal
Salary
Undisclosed
Department
Data Science & Analytics
Employment
Contract
Experience
Senior
Published
Apply before
8 Dec 2026
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35
Application actions
1
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Opportunity details

About this role.

 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:

  • 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.

Requirement:

  • Language Skills: Native fluency in Portuguese and fluent in English.

  • Location: Must be based in Portugal.

  • 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. 

  • 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. 

  • 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. 

Optional / Preferred Skills: 

  • 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. 

Compensation

Additional Information

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe role combines advanced LLM prompt optimization, structured-output evaluation, error analysis, and ownership of production launch decisions. It requires independent work with proprietary tools and resolution of failures where automation cannot produce a satisfactory result.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per hour)Market rate
$55
US market range (per hour)$45–$75
AI insightThe hourly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The hourly cadence and USD market estimate are inferred for a senior, part-time freelance prompt-engineering engagement in Portugal; estimated market range is $45–$75 per hour, with a midpoint of $55 per hour.

Core skills

Skills and capabilities most closely associated with this opportunity.

Prompt engineeringLarge language modelsLLM evaluationAutomated Prompt GenerationAutomated Prompt OptimizationPrompt testingStructured output designFew-shot learningClassification modelingSQLError analysisF1 score analysisPrecision and recallComputational linguisticsTechnical documentation
Sample interview questions
How would you evaluate whether an autorater prompt is ready for production?

I would first run it against representative gold data and compare outcomes with the human baseline. I would measure precision, recall, F1, disagreement patterns, and performance across important edge-case segments. I would approve launch only after the metrics meet defined thresholds and the remaining errors are understood, documented, and acceptable for the use case.

What do you do when an automated prompt optimization tool reaches a plateau?

I inspect the optimization history and error clusters to determine whether the limitation comes from ambiguous instructions, insufficient examples, schema constraints, or problematic source templates. I then create targeted manual prompt variants, add contrastive few-shot examples where appropriate, and test changes against the gold set. I document the plateau, hypotheses, experiments, and final recommendation.

How would you design prompts for strict structured outputs in a complex classification task?

I would define an unambiguous output schema, explicit label definitions, decision rules, and handling for missing or conflicting evidence. I would use carefully selected examples that cover normal cases, boundary cases, and common anti-patterns. Finally, I would validate formatting compliance and classification quality separately so a well-formed output does not conceal weak reasoning or incorrect labels.

Describe how you would investigate a low F1 score for a newly migrated template cluster.

I would break down false positives and false negatives by template, label, language, and input pattern, then review representative examples with the human-rating criteria. This helps distinguish prompt defects from unclear taxonomy, data quality issues, or inconsistent human labels. I would prioritize the largest and highest-impact error groups, test focused revisions, and confirm improvements on a held-out evaluation set.

How do you communicate a technical launch-readiness decision to non-technical stakeholders?

I provide a concise recommendation supported by the purpose of the workflow, evaluation coverage, key quality metrics, known limitations, and mitigation steps. I translate model behavior into practical business risk, clearly stating whether the system is ready, conditionally ready, or requires further work. The accompanying documentation includes reproducible evidence and ownership for post-launch monitoring.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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