GenAI RAG Engineer Document- Complex Document Parsing & AI Agent
Publicada el 2026-07-27
Descripción de la oferta
We are seeking an experienced GenAI / LLM Application Engineer for a 4-month contract to build a Proof of Concept (PoC) and MVP for a specialized document intelligence and gap analysis AI agent in the healthcare/life sciences space.Preferably Hyderabad based. The core objective is to ingest complex, multi-page regulatory documents (PDFs with dense tables, scanned text, and structured metadata), run automated gap analysis against reference standards, and generate precise, audit-trailed responses with exact page-level citations. Scope • Architect an end-to-end, production-ready RAG workflow in Python (LangChain or similar) that integrates an LLM of your choice with an efficient vector store. • Build the parsing layer able to handle varied layouts, feed the cleaned content into the embedding store, and expose clear APIs for downstream use. • Create an autonomous agent that answers user questions, routes tasks, and continuously improves via feedback loops. • Optimise for low latency and high accuracy while keeping the stack modular so future models or data sources can be swapped in easily. Primary goal The whole effort is about reducing manual work. Speed and accuracy are welcome side benefits, but automation ranks first. Deliverables (acceptance criteria) 1. Fully documented codebase in Git with reproducible environment files. 2. Deployed RAG service (Docker/Kubernetes acceptable) with sample queries that return correct, sourced answers. 3. Agent demo script showing automated extraction, classification, and summarisation on at least three unseen documents. 4. Performance report highlighting latency, token usage, and error-handling strategy. 5. Short hand-over session plus written runbook for our internal dev team. Contract length: 3 months, starting as soon as you’re available. I’m ready to move quickly once we agree on the approach, milestones, and success metrics.
Skills
Fuente original: freelancer