I direct the strategic technical vision and delivery excellence for some of the most complex enterprise transformations across APAC and beyond — partnering with C-level executives, boards, and transformation teams to architect and govern next-generation AI and cloud platforms.
Architecting generative AI ecosystems and agentic AI at enterprise scale — designed to be governed, secure, and trusted from day one.
Read more → 02Building the responsible-AI frameworks and guardrails that let boards and regulators say yes to AI at scale.
Read more → 03Holding delivery to a uniformly high bar — secure-by-design, observable by default, automated, and measured. The platform discipline that turns ambitious AI roadmaps into production reality.
Read more → 04Partnering directly with C-level leaders and transformation teams to set technical vision and hold delivery to the highest bar.
Read more →I lead the architects and engineers who turn the AI frontier into enterprise reality.
The work isn't only technical. It's translating ambition into platforms that are secure, resilient, and accountable — and building the world-class, diverse teams that can deliver them at the highest levels of customer impact.
Accelerated time-to-innovation, with AI moved safely from pilot to production.
Strengthened security postures across cloud and AI estates.
Platforms built to stay reliable under enterprise-scale load and scrutiny.
Cost optimization that holds up as platforms grow.
New business and revenue models unlocked by AI and cloud.
World-class, diverse teams capable of sustaining all of the above.
The prompt era is ending. What replaces it isn't a cleverer prompt — it's engineering. Five foundations separate a demo that impresses from a system you can run in production: memory, state, orchestration, governance, and evaluation.
Read →Autonomy is only safe when you can verify it. 'Be careful' isn't governance. Here are the four concrete controls that turn a hopeful leash into one you can actually inspect: opt-in execution, a verifiable leash, soul files, and live guardrails.
Read →The adapter is the most consequential Paperclip setting and the least discussed. It decides how much machinery sits between your agent and the model. I wired my fleet all three ways — a bare Copilot CLI, a Hermes kernel wrapping it, and an OpenClaw gateway — and one of them quietly broke and started leaning on another. Here's the honest trade-off, and how to choose.
Read →A two-part session for UNSW students. First hour: an industry view of agentic AI, with Microsoft case examples, Responsible AI principles, and the tooling I use for proof-of-concept work. Second hour: live mentoring on student project ideas.
Mid-program feedback session. Reviewing student progress, unblocking technical decisions, and sharpening the path to the final showcase.
The capstone presentations — students share what they built, the design decisions behind it, and the lessons they took from the program.
I'm Regional CTO and Director of the Azure Cloud + AI Architecture practice at Microsoft Services, leading high-caliber architects, engineers, and cross-functional partners across APAC and beyond.
I'm an avid reader and a lifelong student of both technology and the financial industry. Away from the keyboard, I tinker in the open — including copilot-agents-dojo and copilot-cowork-dojo, small open-source projects on making AI coding agents behave with the discipline of senior engineers and collaborate as a team.
Always architecting the future — and deeply grateful for the teams and customers who make it possible.