- I worked in Cloud(OpenStack-based), Cloud Native(K8s-based, ServiceMesh), Microservices
- I’m currently working/learning GenAI: LLM, Context Engineering, Deep Learning, MAS. GenAI System Engineering, AI Native, AI Runtime.
- How to reach me: hobbytp@gmail.com/hobbytp@163.com
- Tech Stack: Python/Typescript/C/C++/Go/Shell, Docker/K8S,LangChain/LangGraph, AutoGen, CAMEL, RAG, Finetuning, Databases, Messagebus, Gateway, Istio, Envoy.
Full-Stack GenAI & Distributed Systems Engineer
From OpenStack & Kubernetes foundations to Multi-Agent (MAS) orchestration and Context Engineering.
🔭 Core Focus:
- GenAI System Engineering: Retrieval, RAG optimization, prompt/context compression, finetuning (PEFT/LoRA), evaluation.
- Multi-Agent Architectures: Planning + tool routing + adaptive memory strategies, Agentic Context Engineering
- Cloud Native & Infra: OpenStack, Kubernetes, Service Mesh (Istio/Envoy), microservices resilience, AI Infro
- Bridging Infra ↔ AI: Turning distributed platforms into intelligent, automatable systems.
- Designed end-to-end RAG pipelines (hybrid retrieval + semantic rerank + context window budgeting), Agentic RAG.
🌱 Selected Strengths:
- Built MAS prototypes integrating LangGraph + custom evaluators for decision refinement.
- Experience in performance-tuning service mesh traffic & observability pipelines.
🚀Tech Stack (condensed):
Languages: Python, Go, TypeScript, C/C++, Shell
AI: LangChain, LangGraph, AutoGen, RAG, Finetuning
Infra: Kubernetes, OpenStack, Docker, Istio, Envoy
Data & Messaging: PostgreSQL, Redis, Kafka, etc.
Tooling: CI/CD, Observability (Prometheus/Grafana/OpenTelemetry)
Current Exploration:
- Agent evaluation frameworks
- Agent RL
- AI Coding Assistant
📫 Contact: hobbytp@gmail.com | Blog: https://hobbytp.github.io/