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Yash Rupani

@yashrupani

Software engineer (M.E. CS, Oregon State) who builds backend APIs, RAG/AI systems, and real-time data pipelines in Python, FastAPI, and Kafka.

United States

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What I'm looking for

I'm primarily looking for backend software engineering roles, entry to mid-level. I'm also open to roles that blend in AI engineering or data engineering — such as backend engineer, data engineer, ETL engineer, analytics engineer, or AI/ML engineering positions — as long as backend development is a core part of the work.

I'm a software engineer with an M.E. in Computer Science from Oregon State University, currently building backend systems and AI-driven data pipelines. My background spans QA and backend testing at Infosys (validating IVR/IVA systems for Charter Communications), through to hands-on engineering roles building production systems from the ground up.

Recently, I built a document-parsing pipeline at QuickGrants that cut manual data extraction by 80% using OCR-based chunking, and modeled the output in Neo4j for client-facing demos. At GrantAide, I architected a RAG system with FAISS vector search that improved retrieval accuracy by 40%, and shipped Flask/Firestore APIs across AWS and GCP with 99.9% uptime. I'm also a Research Assistant at OSU, co-developing a framework for modeling emotional attunement in AI-mediated conversations, work accepted at AMCIS 2026.

On the side, I build real-time data systems for fun, a Spark/Kafka-based e-commerce lakehouse and a WebSocket-based crypto market monitor processing 500+ events/second.

I'm looking for backend software engineering or data engineering roles where I can keep working across APIs, distributed data systems, and AI infrastructure.

Experience

Work history, roles, and key accomplishments

Current

Research Assistant

Current

Oregon State University

Oct 2025 - Dec 2025 (2 months)

Co-developed a Python framework to operationalize "alliance variance," a metric modeling emotional attunement and goal alignment in AI-mediated conversations, in the paper accepted at AMCIS 2026.
Prototyped agentic AI system processing de-identified psychiatrist-patient transcripts to monitor relational-dynamics signals in real time, for automated evaluation of AI-mediated mental health support.

Python ETL AI Agents RAG Data Validation Indexing Strategies Agentic AI

Current

Software Engineering Intern

Current

QuickGrants

May 2026 - Present (3 months)

Built an automated document-parsing pipeline in Python (FastAPI), reducing manual data-extraction effort by 80% via OCR-based text chunking across 100+ page policy documents, tuning extraction accuracy to 95%.
Modeled parsed policy data in Neo4j and deployed on Render with Cloudflare storage, cutting demo prep time by 15 hrs/week, transforming unstructured text into structured, queryable reports.

RAG Python Render Cloudflare Neo4j fastAPI

AI Agent RAG Intern

GrantAide

Jul 2025 - Sep 2025 (2 months)

Architected a RAG system using FAISS vector databases and Python, applying system design principles to power grant discovery, improving search relevance and retrieval accuracy by 40% over keyword search.
Engineered and deployed Flask + Firestore backend APIs across AWS/GCP multi-cloud environments, reducing API response latency by 30% and cross-region latency by 25% while maintaining 99.9% uptime.

Flask Firestore Faiss Vector Search AWS GCP

Senior Systems Engineer

Infosys

Jun 2021 - Jun 2023 (2 years)

Tested backend APIs and call-routing logic for an IVR/IVA platform (Charter Communications), 70-80 cases per cycle.
Automated backend regression testing, saving QA 5-10 hrs/week and speeding up releases.
Validated large-scale backend datasets via SQL, ensuring data consistency across IVR/IVA workflows.
Collaborated with a 4-5 person Agile QA team in JIRA, catching 70-80% of defects before release.