Meihao Cheng
97 GPA), graduating December 2025. She specializes in backend development, AI integration, and distributed systems, with hands-on experience building LLM-powered applications and scalable microservices architectures.
About
Meihao Cheng is a Computer Science Master’s student at Northeastern University Vancouver (3.97 GPA), graduating December 2025. She specializes in backend development, AI integration, and distributed systems, with hands-on experience building LLM-powered applications and scalable microservices architectures.
During her internship at Traction Complete, Meihao developed AI-powered Chrome extensions that reduced manual research time by 75%, integrating multiple LLM providers (OpenAI, Anthropic, Perplexity) with Google Sheets for automated CRM enrichment. She also built a Salesforce AI chatbot using Apex and Lightning Web Components.
For her capstone project with Regie.ai, she developed a 4-stage LLM-driven sales intelligence system using FastAPI and PostgreSQL that automates B2B company research and outreach generation. Her technical toolkit includes Python, Go, TypeScript, AWS (ECS, Lambda, RDS), Docker, Kubernetes, and various databases.
Meihao brings a unique perspective to tech, having transitioned from a business background with experience in B2B e-commerce and social media management at Alibaba. She’s actively seeking new graduate opportunities in backend, full-stack, and AI development roles.
- Joined January 2026
Industry experience
- Software & Internet
- Non-Profit Organization
- Healthcare
- Education
Work experience· 5 roles
Architected a distributed microservices e-commerce system using Go and RabbitMQ to handle 200K+ requests with asynchronous processing; deployed on AWS with Terraform, Docker, and ECS; configured ALB/NLB load balancing, RDS persistence, and CloudWatch monitoring.
Architected an LLM-driven backend to automate B2B sales intelligence workflows, reducing manual research time from hours to minutes per profile. Implemented multi-LLM orchestration, exponential back-off retry logic, and structured outputs for downstream tooling.
Developed a Chrome extension with React and TypeScript integrated with Google Sheets API, enabling AI-powered batch data enrichment across multiple LLM providers (OpenAI, Perplexity, Anthropic) to streamline internal research workflows. Implemented concurrent request handling to coordinate batch processing and integrated backend logic to transform LLM outputs into structured data for downstream analytics.
Education
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