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Welo Global

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

AI Summary
This freelance Senior Prompt Engineer role leads the migration of complex templates to LLM-based autoraters. The work combines automated prompt generation and optimization with hands-on prompt design, testing, and refinement for difficult edge cases. The engineer will evaluate model quality against gold data using F1, precision, recall, and human-rating disagreement signals. The position requires native Dutch, fluent English, substantial LLM prompt-tuning experience, and the ability to rapidly adopt proprietary tooling with minimal supervision. The candidate must be based in the Netherlands and will prepare technical launch-readiness documentation.
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Remote from
Netherlands
Salary
Undisclosed
Department
Data Science & Analytics
Employment
Contract
Experience
Senior
Published
Apply before
8 Dec 2026
Listing views
29
Application actions
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 Dutch and fluent in English.

  • Location: Must be based in Netherlands.

  • 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 requires senior-level LLM evaluation and prompt-optimization judgment, including resolving failures where automated tooling cannot produce acceptable results. High autonomy is expected while working with complex template architectures, quality metrics, and launch certification.

Salary analysis

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

Estimated job median (per hour)Market rate
$75
US market range (per hour)$60–$100
AI insightThe hourly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer compensation was disclosed. The hourly cadence and USD market estimate are inferred for a senior freelance prompt-engineering engagement; estimated market range is $60–$100 per hour, with a $75 hourly midpoint.

Core skills

Skills and capabilities most closely associated with this opportunity.

Prompt engineeringLarge language modelsAutomated prompt optimizationPrompt testingLLM evaluationModel performance analysisF1 score analysisPrecision and recallSQLData analyticsClassification systemsTechnical documentationDutch language fluencyEnglish communicationComputational linguistics
Sample interview questions
How would you approach improving a prompt when automated prompt optimization has reached a plateau?

I would inspect failed and borderline examples, cluster error patterns, and determine whether the issue stems from ambiguous instructions, missing constraints, poor examples, or template structure. I would then create targeted manual prompt variants, test them against gold data, and compare precision, recall, and F1 before selecting a change.

How do you evaluate whether an LLM autorater is ready for launch?

I use a representative gold dataset and compare model decisions with human labels across overall and segment-level metrics. Beyond F1, precision, and recall, I review disagreement patterns, high-risk error categories, edge cases, and consistency across prompt versions, then document residual risks and acceptance criteria.

Describe your experience creating prompts for strict structured outputs.

I define a clear output schema, explicit decision rules, valid and invalid examples, and constraints for handling uncertainty. I validate outputs programmatically where possible and iteratively refine instructions when model responses deviate from the required format or classification logic.

How would you use SQL in this prompt-engineering workflow?

I would query evaluation and production data to segment results by template, label, error type, prompt version, and language. This makes it possible to identify systematic failures, calculate quality metrics, prioritize high-impact issues, and verify whether a new prompt improves performance without creating regressions.

What information would you include in launch certification documentation for an LLM workflow?

I would document the prompt version, test data coverage, evaluation methodology, key metrics, known limitations, disagreement analysis, fallback behavior, and approval criteria. The document should clearly explain why the solution meets the quality threshold and what monitoring is required after launch.

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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