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Welo Global

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

AI Summary
Senior Prompt Engineer will lead the technical migration of template workflows to LLM-based autoraters. The role combines automated prompt generation and optimization with hands-on prompt design for difficult template structures and edge cases. It requires evaluating model quality against human baselines using metrics such as F1, precision, and recall, as well as preparing launch-certification documentation. Candidates need native Spanish, fluent English, substantial LLM prompt-engineering experience, and strong analytical capability with SQL or comparable tools. This is a remote freelance engagement restricted to candidates based in Spain.
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Remote from
Spain
Salary
Undisclosed
Department
Software Engineering
Employment
Contract
Experience
Senior
Published
Apply before
8 Dec 2026
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21
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2
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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 Spanish and fluent in English.

  • Location: Must be based in Spain.

  • 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 position requires at least four years of specialized prompt-engineering experience and ownership of complex LLM evaluation and optimization workflows. Success depends on independently resolving tooling failures, diagnosing model-quality issues, and producing technically defensible launch documentation.

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)$40–$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 is inferred from the remote freelance, part-time nature of the role. Estimated market compensation is USD 40–75 per hour, with an estimated midpoint of USD 55 per hour; this is an AI estimate rather than verified salary research.

Core skills

Skills and capabilities most closely associated with this opportunity.

Prompt engineeringLarge language modelsPrompt optimizationAutomated prompt generationLLM evaluationModel performance analysisF1 score analysisPrecision and recallSQLData analysisFew-shot learningChain-of-thought promptingTechnical documentationSpanish language fluencyEnglish communication
Sample interview questions
How would you approach improving an LLM prompt that performs poorly on a complex classification template?

I would first segment failures by template type, label, and error pattern, then compare outputs with gold data and human ratings. I would test targeted changes to instructions, output schemas, examples, and edge-case handling, measuring each version with precision, recall, and F1 before promoting it.

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

I validate that the evaluation set is representative and inspect whether the optimizer is overfitting to a narrow set of examples. I then introduce controlled manual hypotheses, revise the prompt structure or examples, and document the experiments so the next iteration is measurable and reproducible.

How would you monitor disagreement between human evaluators and an LLM autorater?

I would track disagreement rates by task, category, prompt version, and confidence level. I would prioritize recurring or high-impact disagreement clusters for qualitative review, determine whether the issue is ambiguous labeling or model behavior, and use the findings to refine the prompt or evaluation data.

How do you ensure a prompt produces strict, structured output?

I define an explicit schema, specify allowed values and formatting rules, provide representative few-shot examples, and state how to handle missing or ambiguous inputs. I also test adversarial and malformed cases and add automated validation to detect schema violations before scoring downstream results.

How would you explain launch readiness for an LLM autorater to non-technical stakeholders?

I would summarize the task scope, evaluation data, key quality metrics, known limitations, and safeguards in concise language. I would clearly state whether acceptance thresholds were met, identify residual risks, and provide a monitoring and rollback plan for the launch period.

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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