We are looking for a Subject Matter Expert (SME) in Oracle Fusion with a strong background in Enterprise Resource Planning (ERP), specifically in Oracle Financials 24C, to assist in creating high-quality data for the fine-tuning of Large Language Models (LLMs).
The SME will be responsible for developing two types of data sets:
- Scenario-Based Data Sets:
- Develop problem-solving scenarios based on real-world financial issues in Oracle Fusion.
- Each scenario will include a detailed question, context/input, and an expert solution related to common issues such as invoicing errors, exchange rate discrepancies, and user permissions in Oracle Financials 24C.
- The responses should be structured in markdown format for ease of use in LLM training.
- Chat-Based Conversations:
- Create chat conversation examples simulating interactions between users and virtual AI assistants, focusing on finance-related tasks and challenges within Oracle Fusion.
- The conversations should be finance-specific, avoiding general or unrelated issues, to ensure the highest relevance for LLM training.
Responsibilities:
- Develop accurate, finance-specific data sets for LLM training using Oracle Financials 24C.
- Create both scenario-based problem-solving examples and virtual chat conversations.
- Ensure that all content is formatted correctly in markdown and meets the project requirements for LLM fine-tuning.
- Work closely with the team to ensure data quality and alignment with project objectives.
Requirements:
- Extensive expertise in Oracle Fusion, with a deep understanding of Oracle Financials 24C.
- Strong background in Enterprise Resource Planning (ERP), particularly in the finance domain.
- Experience in creating structured, real-world problem-solving scenarios.
- Ability to develop chat conversations simulating user interactions with AI assistants.
- Familiarity with markdown formatting and LLM data requirements is a plus.
- Excellent attention to detail and ability to translate complex financial issues into structured data.