Machine Learning Engineer | Platform & Infrastructure with 10+ years architecting high-availability data systems and ML pipelines for mission-critical utilities. Recently completed M.S. Computer Science (Machine Learning), Georgia Tech. Expert in productionizing ML from ETL to deployment across complex domains like grid operations and financial modeling.
- 🔭 Building production ML pipelines and GenAI tooling (RAG, RL agents)
- 🌱 Deep expertise: PyTorch, Transformers, Lakehouse (Databricks/PySpark), Docker/K8s
- ⚡ Passion: Turning messy real-world data into reliable, scalable intelligence
PG&E | Senior Network Model & Automation Lead (2022-2024)
Architected ADMS graph database for millions of grid assets; built Python automation reducing manual validation 40% across 20+ engineers; deployed anomaly detection on live network models. [file:1]
SCE | Lead Network Model Engineer (2012-2022)
Productionized ML prototypes (Random Forest wildfire prediction, genetic algorithms for circuit phasing); led data standardization cutting redundancy 30%; mission-critical Bash automation for grid control systems.
CL Futures ML Pipeline | github.com/bwang008/CL_Analyst
End-to-end MLOps: ETL → feature engineering (100+ indicators w/ Numba) → LightGBM → walk-forward validation. 15% edge over random on skewed breakout prediction.
GenAI RAG Tool | LangChain, OpenAI API, ChromaDB, Docker
Production-ready internal doc search using retrieval-augmented generation for engineering teams.
RL Agent Pipeline | PyTorch, Gymnasium
Modular continuous-control RL with hyperparameter benchmarking (Adam vs Genetic Algorithms).
Streaming QoS Lakehouse | Databricks, PySpark, Delta Lake
Medallion pipeline for high-throughput telemetry; schema enforcement, PII masking, optimized partitioning.