•Designed and built a multi-agent system that orchestrates workflows across internal CI/CD, version control, and ticketing tools via a unified LLM interface
•Implemented a supervisor agent that routes requests to specialized subagents for tasks such as environment provisioning, code reviews, testing, and workflow automation
•Created reusable agent “personas” that encapsulate complex workflows, significantly expanding system capabilities and adoption
•Standardized tool descriptions and prompt patterns to improve tool selection accuracy and system reliability
•Built structured outputs, evaluation pipelines, and golden datasets to measure and improve model performance
•Developed observability and analytics systems using LangSmith, Mixpanel, and internal data sources to track adoption, performance, and system health
•Upgraded core AI infrastructure and maintained model integrations to leverage latest capabilities
Developer Productivity & Platform Impact:
•Reduced CI/CD pipeline times by ~40%
•Improved local build performance by up to 80% (NX monorepo optimizations)
•Reduced unnecessary CI workloads by ~70% via diff-based build/test strategies
•Built shared libraries and data models (Pydantic) to standardize contracts across services
Leadership & Influence:
•Led design of core features including agent personas, prompt systems, and workflow orchestration patterns
•Mentored engineers and contributed to team growth and adoption of AI-driven development practices
•Partnered with product in a highly ambiguous space to shape direction and accelerate delivery using AI-assisted workflows