✅ Architected an LLM-powered context engineering pipeline converting 5+ enterprise discipline guidelines into structured, AI-ready knowledge assets
✅ Designed and implemented a Multi-Agent Workflow Orchestration system with custom MCP server integration
✅ Delivered production-grade features at 100% test coverage under enterprise security and compliance gates
What is this project about?
OOCL Gen-e2 Methodology Enablement was a software engineering engagement with Orient Overseas Container Line (OOCL) — one of the world's largest container shipping companies. My role was to architect and build the technical infrastructure enabling Gen-e2 AI-first delivery within OOCL's engineering teams.
I designed and built an LLM-powered context engineering pipeline that converted OOCL's enterprise guidelines — spanning 5+ disciplines including architecture, security, QA, and delivery standards — into structured, AI-ready knowledge assets. This gave their AI agents consistent, reliable context to work from, reducing requirements misalignment and enabling standards enforcement across distributed development teams.
I also designed and implemented a Multi-Agent Workflow Orchestration system with a custom MCP server integration — accelerating delivery velocity, improving developer experience scores, and surfacing misaligned requirements before they reached development. All delivery was completed at 100% test coverage under enterprise security and compliance gates.
Takeaway #1: Enterprise-scale AI adoption requires treating context as infrastructure
Enterprise-scale AI adoption requires treating context as infrastructure — the quality of what an agent knows upfront determines the quality of everything downstream.
Takeaway #2: Working within enterprise security gates sharpened my instinct for where AI tooling can flex and where it must be constrained
Working within enterprise security gates sharpened my instinct for where AI tooling can flex and where it must be constrained.
🛠️Tech Stack
Project Management
