Nano-vLLM Architecture Profiler (Open Source Project) | Feb 2026

Systems Analysis: Conducted a deep-dive architectural analysis of the Nano-vLLM (1.2k LOC) inference engine, mapping the lifecycle of PagedAttention and KV-cache block management.

Simulated Performance Benchmarking: Engineered a Mock Execution Engine to simulate GPU kernel latency, enabling the testing of scheduling algorithms and memory fragmentation on CPU-only environments.

Optimization Research: Identified potential throughput bottlenecks in the First-Come-First-Served (FCFS) scheduler during long-form reasoning (CoT) generation; documented architectural improvements for multi-agent orchestration.

Tooling: Developed a Python-based visualizer for tracking KV-Cache block allocation and reference counting in real-time.

• Scalable Workflow Orchestration: Led the architectural refactoring of the Outpost infrastructure provisioning service, transitioning to a distributed workflow model using AWS Step Functions and Lambda. This modernization improved system scalability and significantly reduced maintenance overhead through decoupled, stateful execution.

• Lifecycle Automation: Spearheaded the end-to-end design and development of the Outpost Decommission Service, automating complex hardware retirement protocols and ensuring secure, compliant infrastructure turnover.

• Edge Reliability: Maintained and optimized the Snow OTA (Over-the-Air) Update Service, ensuring high-availability and seamless firmware deployments across globally distributed edge computing devices.

Scaled LLM Reasoning (Loong Project): Engineered an open-source framework for synthesizing and verifying long-form Chain-of-Thought (CoT) data at scale. Successfully applied Reinforcement Learning on logic data to reproduce the state-of-the-art reasoning results of DeepSeek-R1 and Logic-RL, inducing emergent behaviors like self-reflection and verification.

Reinforcement Learning (RL) Involvement: Involved in the development of the ReaL-TG framework, which utilizes RL to optimize language models for explainable link forecasting on temporal graphs. Contributed to designing reward signals that prioritize transparency and logical consistency in model predictions.

Supervised Fine-Tuning (SFT): Leveraged SFT workflows to distill tool-use knowledge into compact models via back-translated traces, enabling high-performance autonomous agent capabilities with significantly reduced inference overhead.

Temporal Planning & Benchmarking: Designed and published the TCP Benchmark, a specialized evaluation suite for measuring LLM performance on temporal constraint-based planning, bridging a critical gap in multi-step reasoning assessment.

Multi-Agent Orchestration: Contributed to the CAMEL-AI open-source ecosystem, focusing on autonomous communication protocols and the deployment of "societies" of LLM agents to solve complex, distributed tasks.

◦ LLM Product E2E Delivery: Led and developed Account Plans from concept to successful implementation,

utilizing cutting-edge LLM techniques such as openai, rerank, RAG, model fine-tuning. Architected a robust system

incorporating unreliable components like scraper services.

◦ Agile & Lean Startup: Conducted customer interviews and rapidly iterated products based on feedback.

◦ Product Collaboration: Collaborated closely with Product Management to ensure technical and product

alignment.

◦ Impact: Spearheaded the development of our most critical product, contributing to nearly 100% of new ARR.

Received over 30 positive customer reviews on G2. (previously known as deep dive)

◦ Designed and Implemented an in-memory cache for merchant metadata to reduce the Redis traffic.

Meanwhile, refactored the relevant data retrieval path to be more maintainable. Reduced AWS Redis costby 90% and AWS ECR Cost by 50%. Reduced the latency of all tier-1 APIs by 50%.

◦ Reduced the unit tests run time by 60% (15 min to 4 min).