Director of Data and AI M3
I own product strategy, architecture, and delivery for M3's data and AI platform and its AI products. I launched an LLM-backed Chart of Accounts Mapper, piloted with approximately 10 customers and rolled out to all customers in August 2026. The product serves 80 customers across 200 properties and reduces a mapping process from weeks or months with roughly five support people to several days or about one week.
I designed the product's human-in-the-loop workflow with confidence scores, approval gates, and feedback-loop learning. Built on Claude Opus 5.5 in Databricks, it uses a shared AI/API serving two products and is evaluated on accuracy, confidence, and top-k accuracy. I also built an MCP-based application that connects Claude to accounting data through REST APIs, Databricks Apps, and OAuth.
I established M3's agentic development workflow and built engineering tools including automated Azure DevOps code review, a backlog-generator skill, and a coding-test application. I diagnosed code-review failures on large pull requests by tracing logs to token limits, then raised limits and chunked large pull requests.
I selected Databricks as M3's enterprise data platform after evaluating Databricks and Snowflake, delivered its first production use case within approximately six months, and designed its Unity Catalog governance model. I also created the SDLC and automated build and deployment pipelines, improved Insight releases from monthly to weekly, reduced bugs by 30%, and manage AI/ML capabilities through AI Gateway, Model Serving, MLflow, usage tracking, Lakebase, and an isolation-forest anomaly engine.