Key developer in the Kubernetes refactoring and modernization of a multi-tiered triage pipeline, typical of large organizations maintaining cyber defense. The system processed a continuous stream of artifacts—executables, scripts, and observables—that required automated enrichment, classification, and prioritization.

Primary contribution was the design and implementation of a self-managed Zeebe broker BPMN-based orchestration layer that replaced ad hoc scripts with structured, declarative workflows. I built support for defining analytic pipelines as composable DAGs, integrating tools such as YARA, Ghidra, and internal classifiers as reusable analytic tasks.

This work focused on a BPMN-based orchestration system designed to streamline triage operations, improve maintainability, and support future scaling:

• Developing the workflow execution engine and task interface

• Managing integration with existing enrichment tools and data sources

• Supporting task routing and branching based on artifact scoring or metadata

Key developer in the Kubernetes refactoring and modernization of a multi-tiered triage pipeline, typical of large organizations maintaining cyber defense. The system processed a continuous stream of artifacts—executables, scripts, and observables—that required automated enrichment, classification, and prioritization.

Primary contribution was the design and implementation of a self-managed Zeebe broker BPMN-based orchestration layer that replaced ad hoc scripts with structured, declarative workflows. I built support for defining analytic pipelines as composable DAGs, integrating tools such as YARA, Ghidra, and internal classifiers as reusable analytic tasks.

This work focused on a BPMN-based orchestration system designed to streamline triage operations, improve maintainability, and support future scaling:

• Developing the workflow execution engine and task interface

• Managing integration with existing enrichment tools and data sources

• Supporting task routing and branching based on artifact scoring or metadata

Key developer supporting an enterprise system where users or agents formulated and maintained seed queries used to track evolving relationships in large-scale network models. This approach is typical of applications in fraud detection, content personalization, and cyber threat intelligence.

My primary role was the implementation of the graph construction and enrichment tier. This tier automated:

• Personalized PageRank-based walks from seed nodes

• Fusion of data attributes across structured and semi-structured sources

• Periodic aggregation via Airflow pipelines to maintain dynamic graphs

• Export of enriched results as spreadsheets or structured datasets

The graphs formed a question-focused view into complex data, evolving with each re-enrichment cycle to serve specific investigative or analytic purposes.