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

AI Summary
Welo Global is seeking a senior German-speaking Prompt Engineer to migrate template workflows to LLM autoraters. The contractor will generate, optimize, test, and manually refine prompts for complex classification and structured-output tasks. Core work includes monitoring human-versus-model disagreements, evaluating quality against gold data, and measuring F1, precision, and recall. The role requires at least four years of prompt-engineering experience, strong analytical ability, SQL familiarity, and independent use of proprietary tools. Candidates must be based in Germany and be fluent in both German and English.
Remote from
Germany
Salary
Undisclosed
Employment
Contract
Experience
Senior
Published
Apply before
8 Dec 2026
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24
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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 German and fluent in English.

  • Location: Must be based in Germany.

  • 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 insightРабота требует глубокого практического опыта с оптимизацией LLM-промптов, оценкой моделей и устранением сложных edge cases. Высокая самостоятельность необходима для работы с proprietary-инструментами и подготовки технической сертификационной документации.

Salary analysis

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

Estimated job median (per hour)Market rate
$65
US market range (per hour)$50–$85
AI insightThe hourly pay period is inferred from the job context; it was not explicitly stated by the employer. Вознаграждение работодателем не раскрыто. Для senior-level freelance Prompt Engineer в Германии ориентировочная рыночная ставка оценена в USD за час; почасовой период выбран как наиболее вероятный для удалённого freelance/part-time формата.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you improve an LLM prompt that has plateaued during automated optimization?

I would inspect failed and borderline examples, segment errors by pattern, and determine whether the issue stems from ambiguous instructions, missing examples, output constraints, or data quality. I would then create targeted manual prompt variants, test them against gold data, and compare precision, recall, and F1 before selecting a revision.

How do you evaluate an autorater against a human baseline?

I use a versioned gold dataset with human-labelled outcomes, calculate agreement and classification metrics such as precision, recall, and F1, and review disagreement clusters qualitatively. I also monitor whether changes improve aggregate metrics without introducing regressions in critical edge-case segments.

Describe your approach to creating prompts for strict structured outputs.

I define an explicit schema, enumerate valid labels and decision rules, include concise positive and negative examples, and specify how uncertainty should be handled. I validate outputs programmatically and iteratively refine instructions using failures observed in representative test cases.

What would you do if proprietary prompt-optimization tooling produced inconsistent results?

I would first reproduce the behavior with controlled inputs and document versions, parameters, and outputs. I would isolate whether the inconsistency is caused by prompt changes, model nondeterminism, data issues, or the tool itself, then use a manual evaluation path while escalating a clearly documented issue to the appropriate team.

How would you communicate launch readiness for an updated prompt workflow?

I would prepare concise certification documentation covering the intended use case, prompt version, evaluation dataset, quality metrics, known limitations, edge-case results, monitoring plan, and rollback criteria. This gives stakeholders an evidence-based view of performance and operational risk.

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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