Priyanshu Rawat
Building production RAG systems with LangGraph
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About
Data Scientist and ML Engineer specializing in production-grade RAG systems, agentic AI, and MLOps infrastructure. Currently at University of Rochester's Center for Integrated Research Computing, building multimodal RAG systems and ML-powered intelligence platforms serving 1000+ researchers.
Expertise in LLM optimization (LoRA/QLoRA fine-tuning, quantization, vLLM), vector databases (pgvector, ChromaDB, Pinecone), and production ML deployment (Docker, Kubernetes, CI/CD). Strong foundation in PyTorch, big data technologies (Spark, Kafka, Airflow), and cloud platforms (AWS).
Recent projects include a cybersecurity threat intelligence system with fine-tuned LLMs achieving 3x throughput improvements, and a Wegmans capstone project predicting gluten sensitivity across 5.6M transactions with optimized business ROI.
Let's connect and collaborate on cutting-edge AI solutions!
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Experience
FLX AI
- Cut financial analysis time from days to hours by engineering an autonomous agent that synthesizes 500+ pages of SEC filings (10-K, 8-K) to generate instant answers
- Engineered an OCR pipeline (90% accuracy on financial tables) using Docling that implemented section-based chunking while preserving and linking citation metadata to enable precise source attribution
- Engineered a RAG pipeline (FastAPI/pgvector) with a ColBERT v2 re-ranker, cutting latency by 75% by implementing the PLAID search engine and dynamic batching
- LangGraph
- FastAPI
- ColBERT v2
- PostgreSQL
- pgvector
- Docker
- Docling
- OCR
- PLAID
Center for Integrated Research Computing, UoR
Current Employer
Greene Career Center, UoR
Insignia Consultancy
Education
University of Rochester
Rochester, New York
Key Coursework
- Machine Learning
- Computational Statistics
- Data Science at Scale
- End-to-End Deep Learning
Graphic Era Hill University
Dehradun, India
Key Coursework
- Machine Learning
- Data Structures and Algorithm
- Deep Learning
- Object Oriented Programming