Featured Work

Projects and initiatives from the past several years.

AI Teammates

AI Product

Led engineering for Asana's AI agent product from private beta through general availability. Managed 23 engineers across 3 teams. Identified misaligned scope in the first weeks and refocused launch criteria on reliability and core capabilities – shipped GA on time. Improved execution reliability from 86% to 99% and exceeded user retention targets.

  • GA launch delivery
  • Reliability engineering (86% → 99%)
  • Scope correction
  • Exceeded retention targets
Agentic AI Product Leadership Reliability GA Launch

AI Vendor Partnerships

Strategic Partnerships

Built and led Asana's relationships with Anthropic, OpenAI, and Mistral. Outcomes include Asana being featured in Anthropic's Claude 4 launch video, being among the first companies to ship a production MCP server, and early access programs that directly enabled key product capabilities.

  • Anthropic partnership (Claude 4 launch feature)
  • Production MCP server
  • OpenAI partnership
  • Mistral partnership
Partnerships Anthropic OpenAI MCP

RAG Platform Development

AI Infrastructure

Led development of Asana's RAG platform, the shared retrieval layer that now powers all of Asana AI. Scaled from a single search feature to a shared framework – 35x traffic growth, now at 320k weekly requests.

  • Advanced vector search capabilities
  • Context-aware document retrieval
  • Cross-application integration framework
  • Cut p95 request latency of onboarded features by >50%
RAG LLMs Vector DB Performance

AI Evaluation System

Quality Assurance

Developed and led Asana's large language model testing and evaluation program, creating a comprehensive framework for ensuring AI feature quality and reliability.

  • Automated evaluation suite
  • Quality metrics and benchmarking
  • Pre-release model testing
  • Strategic partnerships with Anthropic and OpenAI
Evaluation LLMs QA Benchmarking

Agentic Context Engineering

Performance Optimization

Led development of context engineering techniques for agentic AI systems, improving performance and quality while cutting operational costs. The program led to a patent application filing.

  • 24% improved time to first token (TTFT)
  • Enhanced evaluation pass rate into the high-90s
  • 35% LLM budget reduction
  • Context optimization algorithms
Agentic AI Performance Cost Optimization Context Engineering

ML Recommendation System

Machine Learning

Implemented a recommendation system for Shift's car marketplace that measurably moved primary conversion metrics.

  • 25%+ increase in primary conversion rate
  • Personalized recommendation algorithms
  • User behavior analysis
  • A/B testing framework
ML Recommendations Conversion Product

Bit Vector Algorithm

Performance Engineering

Designed and implemented a custom bit vector-based algorithm that achieved a 1000x performance improvement for critical filtering and sorting operations.

  • Reduced processing time from 7s to 7ms
  • Optimized memory usage
  • Scaled to handle large datasets
  • Improved user experience
Algorithms Performance Optimization Scalability

Third-Party Integrations Framework

System Architecture

Extended Asana's RAG platform into third-party data sources, adding connectors for external tools alongside Asana-native content.

  • Google Drive integration
  • OneDrive connectivity
  • Amazon Q integration
Integrations API Design Data Connectors Architecture