👋 Hi, I’m @Flink-ddd, a passionate senior engineer with a solid foundation in Java development and microservices architecture, practical Python programming experience, and a growing focus on AI infrastructure and large language model (LLM) applications.
1：Java & System Architecture Microservices & DevOps Expert: In-depth understanding of system design, deployment pipelines, and scalable architecture.
Secondary Development & System Integration: Deep customization of mainstream technology stacks to improve business agility.
Key Projects: Monitoring & Alerting Platform: Built a full-link business monitoring system via secondary development of Prometheus + Grafana, supporting multi-channel alerts (SMS, Email, IM platforms), covering server, API, and business process levels.
High-Concurrency Messaging System: Using Spring Boot, WebSocket, Kafka, Zookeeper, MinIO, built an IM system supporting millions of users with features like group chat, voice/video calls.
Enterprise Search Platform: Leveraged SpringBoot, Elasticsearch, Flink-CDC, MongoDB, to build a high-performance search engine for billions of records.
Knowledge Graph Center: Designed a Neo4j-based system to support evolving business relationships and semantic query logic.
Multi-functional Payment Platform: Developed a group-level payment center using DDD (Domain-Driven Design), handling millions of transactions per day.
Real-Time Data Sync Platform: Built a visualized synchronization platform using Flink-CDC, supporting heterogeneous source sync to MQ/DB with configurable strategies.
2：Big Data Expertise Experience with real-time stream processing, data lake, and OLAP solutions.
Projects: Enterprise-level Flink Cluster: Built a unified Flink-based computing center for real-time data lake, stats center, and ETL pipelines.
OLAP Solutions with ClickHouse: Delivered a responsive analytics experience with massive datasets using ClickHouse.
Log Aggregation Platform: Implemented enterprise-scale log collection via Flume, integrated with downstream analytics.
3：AI & LLM Projects Currently exploring and implementing AI technologies with hands-on focus on:
Model Fine-tuning: Customizing open-source LLMs (e.g., Mistral, DeepSeek, Qwen) via LoRA techniques.
RAG (Retrieval-Augmented Generation): Building intelligent Q&A and search systems by integrating vector databases (e.g., FAISS, Milvus) with LLMs.
LLM Inference Acceleration: Using vLLM, FlashAttention, and RunPod for high-performance deployment.
Recent Work Includes: Training a domain-specific chatbot using Mistral + LoRA + Huggingface datasets. Building end-to-end pipelines for RAG-based medical Q&A systems. Deploying API endpoints for real-time LLM interaction and content generation.
Keep Always Learning
📫 Let’s Connect: 📧 Email: vensenmu@gmail.com 📱 WhatsApp: +65 8130 2719