At Charles Schwab, I build enterprise AI and data platforms that make engineering operations faster and more reliable. I designed a LangGraph-based multi-agent incident investigation platform using RAG, Azure OpenAI, Databricks, and MCP, reducing investigation time by approximately 65%.
I build production AI services with FastAPI, Docker, AKS, GitHub Actions, MLflow evaluation, and governance controls including PII masking, RBAC, audit logging, and access policies. I also connect AI agents to Databricks Jobs, Airflow, and Kubernetes APIs for real-time operational diagnostics.
I modernized brokerage and portfolio data workflows at Charles Schwab with Azure Databricks, PySpark, Delta Lake, and Data Factory. My work improved pipeline throughput by 45%, reduced Databricks compute costs by 30%, and supported 5–10 million records nightly across 50+ tables and seven source systems.
Previously at Kroger and ZS, I built reliable, monitored data pipelines for retail and pharmaceutical reporting. I reduced failure detection from hours to under 15 minutes, strengthened data-quality validation and PCI-DSS controls, and delivered SQL-based consolidation pipelines serving millions of daily records.
