Austin, Texas Metropolitan Area
At Indeed, I built large-scale, ML-driven and event-based systems powering job matching, experimentation, and real-time decision making at global scale.
* Led the design and development of pipelines that convert indexed jobs into hosted jobs, enabling improved user experience, experimentation, and monetization.
* Architected scalable systems processing hundreds of millions of daily events with strong performance and reliability.
* Re-architected ingestion, ML scoring, and routing into loosely coupled, asynchronous services using Kafka, improving scalability and reducing latency by up to 90%.
* Built generic ML platforms that enabled data scientists to deploy and test models in real time, driving faster experimentation and increasing incremental revenue by 30%.
* Delivered a COVID-response MVP for batch ML routing under tight timelines, generating $3M+ in incremental revenue.
* Led Kubernetes adoption and continuous delivery practices, improving deployment speed and developer productivity.
* Mentored engineers and contributed to hiring and technical leadership across teams.