Wei Yang

Lead the development of production AI, enterprise search, data integration, and personalization capabilities for industrial digital platforms.

• AI Chatbot & RAG Systems:
  • Led full-cycle development of a production-grade RAG conversational AI chatbot using Azure OpenAI, Redis, and Weaviate vector database; architected sticky session routing and load balancing to support 1K+ DAUs
  • Designed multi-turn conversational memory with Redis persistence and context window management; built agentic workflows with function calling to orchestrate custom tools and external APIs
  • Implemented security guardrails including input sanitization, content filtering, and PII masking to ensure safe and compliant AI interactions
  • Delivered real-time AI Overview and autosuggest powered by live user queries with caching layer for low-latency responses; built scheduled report pipelines and user feedback loops to continuously improve relevance and quality
  • Engineered scheduled multilingual (I18N) indexing pipelines across 10+ heterogeneous data sources, combining keyword-based and semantic hybrid search with semantic reranking, multi-channel query routing, query expansion, and iterative retrieval
• Infrastructure & Platform Engineering:
  • Architected scalable full-stack infrastructure: VM provisioning, runtime orchestration, and offline pipelines for knowledge base synchronization and cache optimization
  • Maintained high reliability and low-latency performance through proactive monitoring and tuning
  • Applied data-driven insights to evolve CMS architecture and scale web platforms
• Data Integration & Search Optimization:
  • Designed 7+ MuleSoft API integration workflows for incremental delta updates of partner accounts and locations
  • Integrated multi-source data into a Solr and Elasticsearch–based search layer with cache optimization
• Personalization & Machine Learning Pipelines:
  • Developed User-to-Item and Item-to-Item recommendation pipelines with rolling cache and Akamai CDN integration
  • Built offline ML pipelines to optimize sales funnel conversion and marketing campaign targeting
• Content Platform (AEM):
  • Developed licensable software modules on Adobe Experience Manager to streamline content authoring and multi-channel publishing

Language features pattern detection with Deep Learning models, data parsing and statistics modeling

• Cleaned and structured 94,581 earnings call transcripts to explore 96 language factors influencing stock return
• Achieved 72% accuracy on text classification with a domain-adapted BERT language model using PyTorch
• Conducted text analysis, sentiment analysis, and text mining utilizing NLP techniques with SpaCy and NLTK
• Set up and managed a remote GPU environment on Ubuntu for Machine Learning and Deep Learning tasks
• Developed tutorials on implementing deep learning models using related Python packages

Awarded the Provost’s Scholarship during the MSc in Data Science program.

Ranked in the top 6.3% out of 25,558 teams.