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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: Strong 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.