5
1
India
2+ years of experience
I am a CSE Undergrad student passionate about advancing the field of artificial intelligence. My interests lie in machine learning, natural language processing, neural networks, and the evolving landscape of agentic AI. π§ I have built projects integrating multiple AI APIs and I strive to optimize a project towards efficiency and execution. π» Iβm proficient in Python, comfortable across the data science stack, and always eager to experiment with new tools, architectures, and frameworks. π€ Iβm looking to learn and collaborate on cutting-edge projects, research, or opportunities that challenge boundaries and spark innovation.

The Cascade Routing Engine implements a three-phase inference pipeline: Local Phase: Queries are routed to a local model endpoint (default: Qwen/Qwen2.5-7B-Instruct on AMD MI300X) Self-Evaluation Phase: The local model evaluates its own response accuracy on a 0-100 scale Escalation Phase: If accuracy falls below 80%, queries escalate to Fireworks AI with intent-based model selection Zero-cost local inference when quality threshold is met (80%+ accuracy) Real-time Server-Sent Events (SSE) streaming for live process tracking Intent-based routing - automatically selects optimal Fireworks model: >conversational / declarative / computational β lightweight tier (8B model) >rag (research/lookup queries) β frontier tier (70B model) Cost transparency - real-time token usage and pricing display Graceful fallback - serves local response with disclaimer if cloud escalation fails
13 Jul 2026