Jaxon Wang

Designed and deployed 15+ AI solutions using LLMs, RAG pipelines, AI agents, and multimodal models, reducing manual engineering workflows by 45% and improving knowledge retrieval efficiency by 60%. Built scalable ML platforms processing 10M+ IoT events/day and integrated digital twins with cloud infrastructure and MLOps pipelines, maintaining 99.5% model availability in production. Developed predictive-maintenance and anomaly-detection systems that improved equipment failure prediction accuracy by 35% and reduced unplanned downtime by 28%.

Created deep-learning models with PyTorch, TensorFlow, and Keras for computer vision and predictive analytics, improving manufacturing defect-detection accuracy by 32% and achieving 96%+ classification accuracy. Designed real-time data pipelines using Kafka, Spark, Databricks, and SQL to process 500GB+ of industrial data/month, reducing data-processing time by 40%. Developed predictive-maintenance and anomaly-detection solutions that increased asset-monitoring efficiency by 50% and improved maintenance-scheduling accuracy by 30%.

Developed enterprise applications and backend services using Python, Java, C#, .NET Core, REST APIs, GraphQL, and SQL for platforms supporting 100K+ daily requests, improving response time by 35%. Designed cloud-native microservices with Docker, Kubernetes, AWS/Azure, and CI/CD, reducing deployment time from days to hours and increasing release frequency by 50%. Built enterprise, database, and IoT integrations to improve data-synchronization speed by 45% and reduce manual data operations by 60%.