As a Lead Member of Technical Staff at Salesforce, I currently lead the Big Data Compute Services team within the Einstein AI platform, where I architect and manage data lake solutions that provide critical APIs for AI model training. Our platform powers Salesforce's predictive and generative analytics AI engine, enabling teams across the organization to efficiently access and process data for their AI/ML models.
With over 20 years of experience in distributed systems and cloud computing, my expertise spans:
Building enterprise-scale Apache Spark services on Kubernetes (K8s) clusters for Salesforce customers
Developing cloud-native data pipelines processing billions of network events across hundreds of protocols
Architecting in-house distributed key-value databases for streaming network events
Implementing secure, high-performance data infrastructure for AI/ML workloads
Leading CI/CD implementation with Jenkins, terraform, and AWS services
I hold two significant approved U.S. Patents:
"Intelligent dropping of Packets in a Network Visibility Fabric" (U.S. Patent No. 20180091427)
"Network Switch Device for Routing Network Traffic through an inline security tool" (U.S. Patent No. 14/880,036)
My contributions to the field have been recognized through multiple awards, including the Gigamon Innovation Award for UDP Brokering, which generated over $10M in annual revenue. I completed the Stanford Graduate School of Business Ignite Program in Innovation and Entrepreneurship (2018), which enhances my ability to evaluate both technical innovation and business impact.
At Salesforce's Einstein AI division, I lead initiatives to deliver secure, user-friendly, and cost-effective platforms for large-scale data processing, seamlessly integrated with the Salesforce ecosystem. This role, combined with my background in security visibility solutions and network detection systems, provides me with unique insights into both theoretical advances and practical implementations in the field.
My research contributions can be found on my Google Scholar profile: https://scholar.google.com/citations?user=m6cx6HkAAAAJ&hl=en
I am actively helping academia and startups leverage my comprehensive experience in building enterprise-scale distributed systems, cloud-native architectures, and AI/ML platforms. My expertise spans Apache Spark orchestration, AWS cloud infrastructure, network security solutions, and performance optimization of large-scale systems. Combined with my deep knowledge of DevOps practices, microservices architecture, and programming across multiple languages (Java, C++, Python), I provide valuable insights to emerging projects and research initiatives.
I am passionate about advancing technology through published articles to help pave the path toward safe and beneficial AI/ML technology for mankind. With my unique perspective spanning both enterprise AI implementation and foundational distributed systems, I aim to contribute to the rigorous evaluation and development of technologies that will shape our future while ensuring they remain aligned with human values and societal benefits.
