• 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.

• Designed and implemented a scalable, distributed data pipeline to transform large-scale healthcare datasets into standardized models using open-source, cloud-native tools.

• Partnered with production teams to deploy robust, high-throughput solutions for ingesting and processing data into cloud-based analytics platforms.

• Built an automated testing and validation framework for a healthcare analytics SaaS platform to ensure data transformation accuracy and integrity at scale.

• Developed complex ETL pipelines to unify disparate healthcare data sources using Python, RabbitMQ, and AWS services (S3, EC2, Redshift, PostgreSQL).

• Created and deployed test automation frameworks (unit + integration) integrated with Jenkins, supporting continuous delivery and improved release quality.

• Designed and developed a machine learning–based tool to quantify the severity of pediatric lung infections from chest X-ray images, using statistical techniques such as Kullback–Leibler divergence for pattern analysis.

• Applied Bayesian modeling and probabilistic inference to improve classification of 3D anatomical structures in medical imagery.

• Project awarded 1st place in the Graduate Physical Science Division at SFSU’s 2015 Engineering Project Showcase.

• Research published at IEEE-EMBS 2015: "Severity quantification of pediatric viral respiratory illnesses in chest X-ray images."

• Pattern Analysis and Machine Intelligence

• Discrete Mathematics

• Software Development