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Software Engineering Intern at Salesforce
Time frame: June – September 2025
Team: Experience Sites Runtime
Project(s): AI-powered debugging tools
Languages Used: JavaScript, Python, PHP, Java
What I Delivered
LLM-powered debugging agent that accelerated root-cause analysis and fix cycles for production issues.
Custom MCP tool retrieving stack trace metadata, source files, and file ownership mappings automatically.
Structured debugging reports outlining exception details, root causes, impacted files, and remediation strategies.
~9% reduction in cycle time through auto-suggested code fixes in a review-ready format.
Impact and What I Learned
LLM in Production
Learned how to design AI tools that integrate cleanly into real developer workflows rather than existing as standalone prototypes.
Working at Scale
Navigating a large codebase with multiple teams reinforced the importance of code ownership, documentation, and cross-team collaboration.
Measuring Impact
Framing engineering work in terms of measurable outcomes — not just features shipped — was a key shift in how I think about building software.