CV
Yan Paing Oo
AI Engineer & DevOps
+6588102190 · yanpaingoo.dev · LinkedIn · GitHub · [email protected] · WhatsApp
AI Engineer with a Computer Science degree in Knowledge Engineering (AI, ML, Computer Vision) and 3+ years of production experience shipping LLM-powered systems. Delivered multi-agent chatbot architectures, RAG pipelines, and tool-calling agent systems across two companies. Strong DevOps background with AWS infrastructure, CI/CD, containerization, and cloud architecture (GCP certified).
Skills
| Area | Technologies |
|---|---|
| AI / LLM | Multi-agent systems, tool-calling agents, RAG, context engineering, prompt design, LLM benchmarking, embeddings, vector search, LangChain, LangGraph, streaming conversations |
| Models | Gemini, ChatGPT, Mistral, Ollama, CloudWeGo Eino, Google GenAI SDK |
| Cloud / DevOps | AWS, GCP, Docker, Kubernetes, Terraform, Ansible, GitHub Actions, GitLab CI, Nginx |
| Backend | Go, Python, Node.js, TypeScript, NestJS, PostgreSQL, MongoDB, REST API, WebSockets |
| Frontend | React, Next.js, Vite, Shadcn UI, TanStack |
Experience
AI Engineer & DevOps, Aug 2025 – Present
Pluggi
- Designed multi-agent LLM architecture with domain-specialized sub-agents and tool-calling. Achieved 100% tool accuracy and 20% token reduction on a 55-case benchmark I designed.
- Engineered RAG evaluation pipeline using RAGAS. Applied BM25 hybrid ranking with vector search to improve retrieval, reducing answer irrelevancy scores by measurable margins.
- Formulated structured response format system (SUMMARY + DATA + GUIDANCE) for agent outputs. Improved agent quality score from 4.7 to 5.0/5.
- Led prompt engineering strategy across 4 domain areas. Designed few-shot examples, chain-of-thought templates, and context injection patterns per domain.
- Defined LLM evaluation methodology: offline benchmark suite plus production sampling, for pre-deploy and continuous quality.
Software Engineer & DevOps (Part-time → Full-time), Feb 2025 – Jul 2025
Pluggi
- Production chatbot struggled at 10K+ U.S. user scale. Architected AWS load balancing with Nginx reverse proxy on reserved EC2 and spot on-demand nodes. Complaints reduced 50%, uptime improved 97% → 99% at 0.57% cost increase.
- Platform had no backend infrastructure. Led architecture from zero with NestJS. System now handles 500+ daily active conversations.
- Built in-house bot-system builder with React Flow visual UI. Workflow deployment speed increased 3x for non-technical users.
- Co-authored state management system using XState with custom extensions. Stabilized flow execution. Mentored 2 junior developers on backend and RAG systems.
Associate Software Engineer, Feb 2024 – Jan 2025
Brillar Pte. Ltd.
- Chatbot responses were slow and limited to single model/channel. Delivered LangChain-powered solutions integrating ChatGPT, Gemini, and Mistral with real-time streaming across 3 channels. Response latency reduced 20%.
- Gen-AI team’s manual deployments were slow and error-prone. Implemented CI/CD pipelines with GitHub Actions and Docker. Deployment cycle reduced 30%.
- Mobile testing across 50+ devices required manual coordination. Built real-time testing platform integrating Appium (Android & iOS), Selenium, and Tosca. Enabled automated parallel testing across 50+ devices.
Freelance Full-Stack Developer & Infrastructure Consultant, May 2023 – Feb 2024
- 4 clients needed custom inventory and financial tracking systems. Engineered full-stack Warehouse Management System and Car Ledger Software with inventory tracking and financial logging. Delivered all 4 projects to production.
- Ledger systems needed to handle high transaction volumes. Built high-performance Go microservices for data-heavy processing. System handled 100K+ daily transactions.
- Small business clients had no cloud infrastructure or backup strategy. Managed end-to-end deployment with Nginx, SSL, automated backups. Clients gained reliable, secured production environments.
Automation & Performance Engineer, Aug 2022 – May 2023
Brillar Pte. Ltd.
- Manual E2E testing was bottlenecking releases. Executed 150+ automated test scripts using Tricentis Tosca. Test coverage increased 35%, manual testing reduced 40%.
- Singapore payments group faced API throughput bottlenecks. Crafted performance/load test plans using JMeter, identified and resolved bottlenecks. API throughput improved 30%.
- Hong Kong banking system needed reliability validation before production. Led API, load, and stress testing. Achieved 99.9% reliability under production traffic.
Education
University of Information Technology
Bachelor of Computer Science in Knowledge Engineering (AI, ML & Computer Vision)