Phurinut Waranich
I architect and deliver backend systems at scale — from workforce automation platforms to smart building infrastructure. Specializing in Python, Go, and cloud-native technologies with a focus on reliability and developer experience.
I specialize in designing and implementing backend systems that handle real-world complexity — payroll engines with dynamic rule evaluation, IoT protocol adapters for building automation, and distributed job processing pipelines. My day-to-day involves Python, Go, and PostgreSQL on the backend, with React and Next.js when the project calls for a polished frontend. I also maintain a self-hosted Kubernetes cluster on Proxmox as both a learning environment and production-grade infrastructure.
Architect and lead development of backend services powering workforce automation and smart building management platforms. Designed a rule-based payroll integration engine processing complex salary and shift policies across external provider APIs. Built the MIOC building automation system with custom BACnet/UDP protocol adapters and real-time alerting infrastructure.
Delivered end-to-end automation solutions for enterprise clients, including real-time operational dashboards. Designed and implemented REST API integrations bridging heterogeneous enterprise systems.
Developed hardware-to-cloud communication prototypes and contributed to IoT data pipeline architecture. Gained foundational experience in embedded systems programming and cloud API design.
Enterprise-grade smart building management platform with custom BACnet/UDP protocol integration, real-time sensor alerting, and Go-based microservice orchestration serving production workloads.
High-throughput rule engine for salary computation and shift policy evaluation. Processes complex payroll workflows through BullMQ job queues with external provider API reconciliation.
Production-grade Kubernetes cluster running on Proxmox hypervisor with pfSense edge routing, Cloudflare Tunnel ingress, and L2/L3 network segmentation via VLAN and NAT policies.
Data pipeline for cleaning and classifying 11,000+ BACnet device records. Combines Pandas-based ETL with idiomatic Go structs for type-safe database column parsing.
Let's work together
Interested in collaboration or have a project in mind? Let’s connect. I respond within 24 hours.