Leading backend engineering and owning the architecture of core components of Zia Agents, building distributed execution infrastructure for production AI workloads.

• Architected an event-driven agent execution platform that decoupled workflow processing into independently scalable stages, increasing throughput 5× and supporting 500K+ agent executions daily across data centers with the same machine footprint.
• Designed fault-tolerant execution with step-level persistence, enabling failure recovery, resumable workflows, and retry-safe execution without restarting entire agent runs.
• Architected a multi-agent collaboration framework supporting workflow-based orchestration, agent handoffs, and session continuity across collaborating agents.
• Designed execution observability infrastructure with ordered step tracking, execution timelines, and metrics pipelines, making previously opaque multi-step AI workflows traceable.
• Engineered a tiered storage architecture for execution data, reducing database storage overhead by 60% while retaining the persistence required for observability, recovery, and long-running workflows.
• Designed a multi-tenant scheduling architecture with per-organization concurrency limits, dynamic burst allocation, and fair resource distribution to mitigate noisy-neighbor contention and prevent high-volume tenants from monopolizing shared LLM execution capacity.
• Led the development of core backend APIs and integrations connecting client applications, internal services, and platform capabilities.

Progressed from UI development to full-stack engineering, owning data visualization systems and key platform integrations for Catalyst QuickML, a no-code MLOps platform.

• Designed and owned a dual-path data processing architecture combining Apache Spark and in-memory execution, improving visualization performance and flexibility when working with large-scale datasets.
• Developed reusable no-code ML pipeline templates and REST APIs for machine learning model lifecycle management, enabling streamlined ML workflows and cross-data-center synchronization.