Senior LLM Engineer Needed – Build Evidence-Grounded Lit Review System (500 Academic Papers pdf)

I am a researcher conducting a large-scale literature review based on academic papers (PDFs).

I already have a predefined set of research questions. I am not looking for AI to write the review. I am looking to build a robust, reproducible system that:

• Applies my question set to each paper

• Extracts answers directly from the documents

• Provides supporting real page references

• Flags missing or ambiguous information

• Outputs everything into a structured master Excel file

This system will function as an additional analytical layer — an “extra pair of eyes” — to support, our review process.

Deliverables

The final output must be a master Excel file with a structured format. In addition, I will also need:

• Clean, documented source code

• Clear instructions to re-run the pipeline

• Ability to add new PDFs and re-run

• Ability to modify or add questions

You should be comfortable with:

• Scientific PDF i

• Extracting data from tables inside PDF

• LLM outputs with enforced JSON schema

• Hallucination mitigation strategies

• Citation grounding at chunk level

Python preferred.(others are also ok)

To Apply, Please include:

1. A description of a similar system you built.

2. Your proposed architecture and tool stack.

3. How you will prevent hallucinations.

4. Estimated timeline

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