I led a 30x year-over-year adoption surge for PBCentral (pbcentral.amazon.com) by transforming it into a growth engine for Amazon Private Brands. We reduced supplier onboarding time from 45 days to 5 by redesigning the platform around an AI-first architecture, accelerating time-to-value and conversion. Through rigorous A/B testing, funnel diagnostics, and cohort analysis, we identified and eliminated key drop-off points, increasing supplier activation and engagement across more than 3,000 global partners.

To drive user retention and streamline access, I led end-to-end re-architecture of onboarding, identity, and access control. We automated invitations, verification, and authentication flows, integrating RBAC, ABAC, SSO, and Passkey standards. This reduced friction across the supplier lifecycle, boosted active user retention, and improved security posture.

I also owned the LLM automation charter, launching multi-agent MCP-based workflows that cut claims evaluation cycle time from 30 days to 5. These agents optimized response relevance and throughput, accelerating speed-to-market and reducing manual intervention.

To support this growth and technical complexity, I scaled the org to over 15 engineers and PMTs, driving hiring bar-raising, delivery predictability, and coaching top talent into senior roles.

I led the 0→1 build and launch of the Private Brands PLM workflow platform — scaling it to over 100,000 annual tasks, 1,000+ monthly users, and tracking $1B+ in product development across 100+ brands. We built the platform from the ground up as a configurable orchestration engine, partnering closely with Process Engineers, UX, and SDEs to simplify complex product workflows and enable rapid iteration and scalability.

To improve speed-to-market and reduce friction, I developed a throughput acceleration model that leveraged dwell times, SLA breaches, and team capacity metrics. This data-driven approach identified key blockers and enabled targeted automation of high-friction workflow steps — ultimately doubling task throughput and contributing to hundreds of millions in projected GMS uplift.

I also introduced ML-based task prioritization, training ranking models on historical task data (e.g., resolution times, dependencies, business impact) to intelligently sequence backlog execution. This maximized delivery velocity and ensured high-leverage work received timely attention, improving platform responsiveness and planning predictability.

I drove alignment at the VP and Director level through clear metrics, PRFAQs, and benchmarking studies. The platform achieved the highest CSAT across the PLM suite and laid the groundwork for future-facing externalized supplier systems.

I built the technical foundation for Private Brands PLM tool at scale. I created data pipelines that processed more than 100 million records, enabling deep performance and quality insights for product and sourcing teams across thousands of ASINs.

I built task and team management services that coordinated more than 100,000 product development tasks across Process Engineers, designers, sourcing teams, and QA partners. These systems became critical infrastructure and formed the backbone for the PLM workflow platform I later owned as a PMT and Engineering Manager.

I redesigned Amazon's return routing and carrier selection engine, introducing dynamic load balancing and improved rule evaluation. This reduced latency by 70% and cut configuration time by 66%, improving customer experience and operational cost efficiency across the reverse logistics network.

I led development of the workflow system used by more than 500 Whole Foods and partner stores to manage return containers. This distributed system handled 15% of all US customer returns and required building resilient pipelines, monitoring, and automated recovery to support store operations at scale.