Consultant - Gen AI Architect (Senior RAG - Document AI Engineer)
Chryselys (company site, opens in a new tab)6–10 yrsHyderabadOn-siteDirect career pageEst. 20–35 LPA
Posted on 9 Sept
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Job description
Structured overview of role & requirements
About This Role
- Build and operate production-scale Retrieval-Augmented Generation (RAG) systems processing 100K+ documents with millions of chunks, ensuring retrieval quality, latency, and scalability.
- Develop and maintain document AI services including parsing, OCR, vision language models, chunking, metadata extraction, embeddings, indexing, and hybrid retrieval techniques.
- Implement and improve evaluation strategies using metrics like Recall@K, nDCG/MRR, and groundedness to enhance retrieval quality on cloud-native AI architectures leveraging AWS services.
Minimum Requirements
- 6–10 years of software engineering experience with at least 2 years in production retrieval and Document AI systems.
- Strong Python development skills with Python 3.11+ and FastAPI for production-grade API/service development.
- Experience with document parsing/OCR (PDF, PPTX, DOCX) and building retrieval pipelines including vector/dense search and BM25 hybrid retrieval.
- Experience working with AWS cloud services (S3, ECS/EKS, Lambda, Bedrock, Textract, OpenSearch) and tools like Docker, pytest, Git, and CI/CD.
Ideal Candidate Profile
- Experienced in scaling and optimizing RAG systems for high document volume and complex retrieval workloads.
- Strong engineering discipline with focus on testing, automation, and production reliability in cloud-native environments.
- Prior exposure or domain experience in Life Sciences, Healthcare, or Pharma preferred but not mandatory.