About this role.
Welo Global seeks a senior Lead Prompt Engineer to lead a technical migration from templates to LLM-based autoraters. The role combines hands-on prompt design and optimization with oversight of APG/APO workflows, gold-data evaluation, and model-quality measurement using F1, precision, and recall. The lead will mentor junior engineers, troubleshoot difficult template and tooling edge cases, and prepare launch-certification documentation. Candidates must be U.S.-based, have native English fluency, an advanced degree in a relevant analytical discipline, and at least seven years of prompt-engineering experience.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
5/5Salary analysis
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Core skills
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Sample interview questions
I would first map the decision logic, dependencies, expected output schema, and known edge cases. I would generate an initial baseline prompt, evaluate it against a representative gold set, then iteratively refine instructions, examples, and output constraints while tracking quality metrics by template segment.
I verify that the evaluation data is representative and inspect error clusters rather than relying solely on aggregate scores. I then identify whether the issue is caused by ambiguity, missing context, schema constraints, adversarial inputs, or model limitations, and apply targeted manual prompt revisions or escalate tooling defects where appropriate.
I would compare its outputs with human-labeled gold data using precision, recall, F1, agreement rates, and error severity. Launch readiness should also include threshold-based acceptance criteria, analysis of high-risk failure modes, monitoring plans, and documented justification for any remaining deviations from the human baseline.
I would establish clear prompt-design standards, review processes, experiment documentation, and metric definitions. I would pair mentoring with practical feedback on error analysis and prompt revisions, while gradually assigning ownership of bounded template clusters so engineers build sound judgment and autonomy.
I define an explicit schema, tightly constrain valid values and formatting, provide representative few-shot examples, and separate instructions from source content. I test malformed, ambiguous, and boundary inputs, then use automated validation and error analysis to improve both prompt robustness and downstream handling.
We are looking for an experienced Lead Prompt Engineer to guide and manage a team through the full technical migration process, transitioning templates to LLM autoraters. In this role, you will leverage advanced prompt engineering techniques and the client’s internal tools to optimize model performance, ensuring the successful integration and ongoing enhancement of AI systems. As the team lead, you will drive the strategy, mentor junior engineers, and play a key role in shaping the future of our AI-driven solutions.
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 English.
Location: Must be based in United States.
Education: 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 7 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
Federal Law Compliance
In compliance with federal law, all persons hired will be required to:
– Verify identity and eligibility to work in the United States; and
– Complete a required employment eligibility verification form.
Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.
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