We seek an AI Prompt Engineer with strong analytical skills, excellent reading comprehension, and exceptional attention to detail. You will design, test, and refine prompts for large language models, audit AI-generated outputs for accuracy, and work closely with client teams to ensure AI solutions meet production quality standards.
Key Responsibilities
Prompt Engineering and Design
- Design and refine structured prompts for AI models to extract data and generate narrative outputs from complex source documents.
- Identify and diagnose AI failure modes such as fabricated data, incorrect source attribution, and formatting errors, and develop targeted prompt fixes.
- Iterate prompts through structured test-and-audit cycles to meet defined accuracy thresholds.
Quality Assurance and Validation
- Audit AI-generated outputs against source documents, verifying data points at the individual figure level.
- Build validation workbooks to track accuracy metrics across test runs.
- Categorize and document errors by type and severity to drive systematic improvement.
- Work with client subject matter experts to understand workflows, quality expectations, and output conventions.
- Translate domain-specific drafting conventions into structured AI instructions.
- Present validation results and accuracy metrics to internal and client stakeholders.
Required Skills & Qualifications
- Education: Bachelor’s degree in Finance, Accounting, Business, Economics, or a related field.
- Experience: 0 to 2 years including internships, academic projects, or full-time roles.
- Strong analytical and quantitative reasoning with comfort working with numbers, percentages, financial figures, and tabular data.
- Exceptional attention to detail with a natural tendency to validate numbers and verify source accuracy.
- Excellent reading comprehension with the ability to interpret dense, data-heavy documents such as financial reports and regulatory filings.
- Clear, precise, and structured writing skills to create unambiguous AI prompts.
- Proficiency in Excel or Google Sheets for data validation and tracking.
- Patience and discipline for methodical, repetitive quality assurance work.
- Strong logical reasoning to trace AI errors to root causes and implement systematic fixes.