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

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

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

As team lead, enhanced team processes to facilitate more agile deployments and improve communication, including: comprehensive process and API documentation, optimized VQA procedures, streamlined onboarding for new developers, and actionable insights in sprint retrospectives.

Conceived and delivered a two-part presentation to enhance workplace communication and collaboration.

Created and implemented a comprehensive validation system for all form input fields, which enhanced data consistency and provided a smoother user interaction.