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Project Lion – Senior Prompt Engineer – Germany (Remote, Part-Time)
- Remote from
- Germany
- Salary
- Undisclosed
- Department
- Data Science & Analytics
- Employment
- Contract
- Experience
- Senior
- Published
- Apply before
- 8 Dec 2026
- Listing views
- 24
- Application actions
- 0
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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
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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Sample interview questions
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.
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.
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.
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.
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.
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