I am a Generative AI Engineer specializing in the intersection of High-Performance Inference and Complex RAG Orchestration. With 2 years of experience, I focus on transitioning research-grade models into production-ready systems using quantization (AutoRound), vLLM optimization, and stateful agentic workflows.

• Current Focus: Investigating compatibility between AutoRound W4A16 quantization and vLLM for Vision-Language Models.
• Core Philosophy: "Optimization is not just about speed; it's about making advanced AI architecturally sustainable."

• Quantization: Advanced W4A16/INT4 quantization using AutoRound.
• High-Throughput Serving: Production deployment of LLMs/VLMs via vLLM.
• GPU Efficiency: Memory-efficient serving and compute optimization for NVIDIA RTX 3090/4070/5090 and AWS G5 instances.

• Hybrid Retrieval: Orchestrating BM25 (Elasticsearch) and Dense Vector Search.
• Advanced RAG: Implementation of LightRAG and GraphRAG architectures for complex document intelligence.
• Infrastructure: Building scalable backends with PostgreSQL, Neo4j, and FastAPI.

• Stateful Agents: Designing multi-step reasoning pipelines using LangGraph.
• Tool Integration: Autonomous agent systems with persistent memory and complex tool-calling capabilities.

Active contributor to the HKUDS/LightRAG ecosystem, focusing on enterprise-grade integrations:

• Gemini Integration: Implemented the Google Gemini demo for the core framework.
• Storage Backends: Developed the PostgreSQL-backed LightRAG implementation.
• Enterprise Features: Added workspace isolation demos for multi-tenant knowledge management.
• PRs: #2538 | #2556 | #2615

Technical investigation into the export pipelines for quantized Vision-Language Models:

• Analyzing AutoRound W4A16 behavior with Qwen3-VL-8B.
• Debugging AWQ export compatibility for seamless vLLM integration.
• Issue: intel/auto-round #1377

• LinkedIn: vishva-r
• Instagram: @justt_vishva
• Portfolio: GitHub Repositories

⭐️ Building the infrastructure that makes AI smarter, faster, and more accessible.