• Leading backend architecture and implementation for AI Workbench, a next-gen spreadsheet-like platform for building and running LLM workflows.
• Designed and built scalable backend services for execution runtime, metadata management, and dynamic DAG orchestration (AI, Prompt, Expression, SObject columns).
• Integrated Temporal to support long-running, dependency-aware column execution with real-time progress tracking.
• Shaped the architecture for authoring and executing large-scale LLM workflows, collaborating across product, frontend, and platform teams. The platform was showcased at TrailblazerDX 2025.
• Led the design and implementation of Agentforce Agents APIs, the first system enabling Salesforce Flows to dynamically invoke LLM-based agents. Architected the solution to support dynamic agent generation and execution, pushing the boundaries of existing Salesforce technology. This work was showcased at Dreamforce 2024 and TrailblazerDX 2025.
• Architected and led development of a highly scalable, cloud-based data platform for security telemetry, now widely adopted as the standardized ingestion and reporting layer.
• Managed and mentored a team of engineers, applying best practices in distributed system design, cloud architecture, and data pipeline development.
• Deployed the platform across public and private cloud environments using Python, Apache Airflow, Celery, RabbitMQ, Terraform, and AWS (Aurora, S3, Lambda, DynamoDB, EC2).
• Architected "Cetus", a next-gen security graph platform, collaborating across teams to build it using Spark, Apache TinkerPop, Kubernetes, and AWS Neptune.
• Contributed to Long Range Planning and Northstar architecture for both the Detection & Response Lakehouse and Salesforce Asset Inventory systems.