Led design and implementation of SaaS solutions across multiple cloud platforms (Azure, AWS, Alicloud, Openstack, Huawei Cloud Stack, and Proxmox)
Managed and mentored engineering teams, ensuring technical solutions aligned with cloud architecture best practices
Utilized Golang and KubeKey to engineer high-throughput Kafka pipelines and automate Kubernetes cluster management
Designed disaster recovery features for virtual machines across private and public clouds
Monitored bare-metal servers using IPMI Redfish APIs, integrating with hardware from SuperMicro and DELL
Built scalable API server solutions leveraging Golang's Goroutines and gRPC Gateway
GolangKubernetesMulti-CloudgRPCKafka
Software Engineer
Jan 2022 - May 2023
AI Power
Developed and optimized APIs using Golang, Gin framework, Swagger, and Postman
Built desktop applications using C# with .NET framework
Deployed and managed RFID systems and server services on Proxmox
Managed and optimized databases including MySQL and Firebase
GolangGinC#MySQLFirebase
Full-stack Developer
Jan 2021 - Jan 2023
Gocviet Co., Ltd (Contract)
Contributed to website development using PHP, HTML, and CSS
Customized WordPress themes to develop new features
Managed Hostinger VPS for website hosting and configured domain environments
PHPWordPressVPSCloudflare
About
Cloud Infrastructure: Expert in cloud solutions across Azure, AWS, Alicloud, Openstack, and Huawei Cloud Stack. Specialized in Kubernetes cluster management, containerization, and microservices architecture.
Development: Proficient in Golang development with extensive experience in building scalable API servers, automation tools, and cloud-native applications. Strong background in CI/CD pipelines and DevOps practices.
Engineered a CUDA-accelerated Python facial recognition pipeline combining Haar Cascade detection with CNN classification for high-performance real-time streaming applications. The system was benchmarked across multiple hardware platforms to optimize for both accuracy and throughput.
Key Achievements
Implemented a hybrid detection system leveraging face_recognition, TensorFlow-GPU, and OpenCV
Optimized for edge computing on NVIDIA Jetson Nano and Raspberry Pi platforms
Achieved significant performance improvements through CUDA acceleration
Conducted comparative analysis across different hardware platforms
Technologies Used
PythonTensorFlow-GPUOpenCVCUDAJetson Nanoface_recognitionRaspberry Pi
Developed an innovative recruitment system that leverages blockchain technology and federated learning to enhance resume verification while maintaining privacy. This IEEE-published project provides a secure, transparent platform for recruitment processes at a cost-effective price point.
Key Achievements
Revolutionized CV verification using Ethereum smart contracts for transparent, immutable record-keeping
Implemented on-device federated learning to maintain privacy of biometric data
Designed scalable microservices architecture with Express.js and MongoDB
Published research in the 2022 RIVF International Conference on Computing and Communication Technologies (IEEE)
recruitment process. Additionally, the system will protect
candidate facial images during facial recognition data
training, ensuring privacy is maintained.
Gift: As an author, I would like to offer you a
version of the above article and additional interesting
features as a token of appreciation for visiting my personal
page, in lieu of registering for a paid account on IEEE
Xplore to access the complete article.
RFID Smart Shelf System
Project Overview
Led the development of an intelligent inventory management system using RFID technology to automate tracking and location of items in real-time. This solution integrated hardware sensors with cloud-based data management for a complete IoT implementation.
Key Responsibilities
Developed C# applications to interface with RFID hardware and process real-time inventory data
Designed and implemented RESTful APIs in Golang to enable communication between the hardware system and databases
Integrated with Google Cloud Platform (GCP) for data storage and processing
Deployed the complete system on Proxmox virtualization platform with automated failover mechanisms
Configured networking, services, and system startup scripts for reliable operation