: Role Overview: Hands-on developer responsible for implementing prompt workflows, data chunking, and maintaining the accuracy of AI outputs.
Responsibilities: Key Responsibilities
- Implementation: Build and test basic to intermediate RAG systems using frameworks like
- LangChain or Autogen.
- Data Processing: Write Python scripts for PDF/document parsing, text chunking, and embedding generation.
- Prompt Engineering: Systematically test and version-control system prompts to achieve reliable model responses.
Qualifications: Required Skills & Qualifications
- Tech Stack: Robust Python programming, familiarity with APIs (OpenAI, Anthropic), basic LangChain/LlamaIndex usage.
- Qualifications: Bachelor’s in CS or related field; 3–4 years software or data engineering experience with demonstrated exposure to LLM APIs.