Intelligent Salesforce Engineering

Over nearly eight years in software engineering, I have progressed from developing enterprise applications to leading solution design, enterprise integrations, and applied research focused on intelligent Salesforce engineering. My work spans Salesforce development, CPQ, AI-enabled automation, Java/Spring services, APIs, and enterprise architecture. A major focus of my recent work has been building software to solve complex Salesforce problems that are difficult to address through conventional tooling. I designed and developed an AI-assisted Salesforce metadata reconciliation framework for multi-org consolidation. The solution processes thousands of metadata components, applies TF-IDF semantic similarity, detects renamed or equivalent components, identifies configuration drift, calculates compatibility and confidence scores, and prioritizes uncertain matches for human review. Manual validation of 250 sampled results achieved approximately 92% precision. I also developed a knowledge graph-based Salesforce CPQ Conflict Detector that connects to Salesforce, extracts and normalizes CPQ data, models rule dependencies, and detects potentially conflicting pricing and configuration logic. In one evaluation, the system analyzed 67 rules across a graph of 12,054 nodes and 9,559 relationships and identified 42 potential conflicts in approximately 6.7 seconds. My research on Salesforce metadata reconciliation was accepted for publication, and my technology innovation work received a Silver award from the Titan Innovation Awards. I have also served in development-lead and solution-engineering roles on major enterprise initiatives.