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LuYao Chen
Junior Software/ML Engineer specializing in AI systems, cloud infrastructure, and applied research
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About
Backend/infra-focused engineer with experience spanning Go-based MCP servers for an AI-assisted Kubernetes on-call diagnosis chatbot and a Python/Flask PagerDuty automation integration. Previously at Tesla, optimized high-volume battery test data in PostgreSQL using JSONB, partitioning, and a timestamp normalization pipeline; also built PyTorch PINN training workflows and achieved a 20x speedup via batch vectorization.
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Key Strengths
- Designed lightweight Flask microservice to integrate PagerDuty with a Go-based MCP system
- PostgreSQL performance/scalability optimization using JSONB indexing and epoch-based table partitioning
- Built normalization service converting raw epoch integers to PostgreSQL timestamps and derived query fields
- Handled evolving/variable experiment schemas via JSONB to avoid rigid relational schema changes
- Integrated ML workflows (PyTorch PINN training) with reusable scripts across mesh coordinates
- Improved PINN training throughput by vectorizing batches (20x speedup)
- Built Kubernetes-focused MCP server enabling natural-language querying of cluster resources with context
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