Experience / 2017—Now
My responsibilities have grown from technical problem-solving to leading AI initiatives, developing people, and aligning product and engineering around a shared outcome.
Leading AI product initiatives spanning multi-agent workflows, recommendations, and semantic knowledge. I work with product and engineering teams across the US, China, Malaysia, and Singapore to turn technical capabilities into useful workflows.
My role also includes mentoring junior ML engineers, participating in technical hiring, and helping lead annual internal AI hackathons with company leadership.
Delivered scoring and recommendation work across approximately 100K locations and 10K market/trade combinations. Built machine learning pipelines, cloud data workflows, and model releases for operational decisions.
Supported data-centric applications and Data as a Service capabilities using Python, SQL, and applied machine learning patterns.
Researched online meta-learning, deep-learning optimization, and continuous model adaptation for smart-grid applications. Ph.D. awarded in December 2021; research continued through January 2022.
Also served as a Financial Engineering teaching assistant across multiple semesters, supporting students through tutoring and grading.
I began as an AI subject-matter expert, helping teams with prompts, technical choices, and implementation. My role was to make an ambitious idea achievable and guide the team toward a stable proof of concept.
My responsibilities expanded to shaping hackathon topics with company leadership. I began asking which challenges addressed current operational needs and offered a practical path to greater efficiency or business value.
As a judge, I consider whether a proposal can move into development, fit realistic delivery cycles, and produce useful impact. Technical feasibility still matters; so does a credible plan for putting the idea to work.
This progression changed how I lead beyond hackathons: understand the real problem, help people test assumptions, and connect a working system to a useful outcome. That approach also shapes my cross-regional call-center AI work and operations workflow project.
See the recognition photo & featured interviewsClarify the user problem and balance quality, latency, delivery effort, and operating risk.
Bring product and engineering perspectives together, mentor colleagues, and guide ideas toward working prototypes.
Design and deliver AI agents, recommendation systems, ML pipelines, and the evaluation behind them.
Progress comes from people who can turn evidence into shared action.Hackathons, recognition & featured interviews