Engineering Manager/Staff Engineer | Cloud & Backend Systems | AI Engineering
Engineering Manager/Staff Engineer with 8+ years of experience in distributed systems, IoT platforms, cloud infrastructure, and engineering leadership.
I focus on building scalable systems, improving engineering execution, and applying AI to real-world engineering and management workflows.
An AI-powered career analysis and knowledge-base platform built with RAG and MAS architecture.
- Hybrid retrieval using Dense Search, BM25, and Reciprocal Rank Fusion
- Multi-agent workflows for resume analysis, interview preparation, career planning, and salary analysis
- Agent loop engineering, supervisor routing, fallback handling, and tool orchestration
- Guardrails, evaluation harnesses, routing analysis, and prompt optimization
- Built with FastAPI, TypeScript, Next.js, Milvus, PostgreSQL, Ollama, and VoltAgent
- Local-first architecture with real-time responses through Server-Sent Events
A reusable collection of AI skills for Engineering Managers, technical leaders, Claude Code, Codex, and other AI development tools.
- Delivery health and project recovery
- Incident communication and postmortems
- Cloud cost and reliability reviews
- Architecture Decision Records
- One-on-one preparation and feedback planning
- Hiring, capacity planning, and engineering metrics
- Cross-team dependencies and stakeholder communication
- Evidence-based outputs with explicit fact, pattern, and hypothesis classification
- Lead and develop cross-functional engineering teams
- Drive technical strategy, architecture, and delivery execution
- Design and scale distributed backend systems
- Build high-concurrency and event-driven data platforms
- Improve cloud reliability, observability, and operational maturity
- Optimize infrastructure cost and system performance
- Introduce AI-assisted workflows into engineering organizations
- Translate business goals into executable technical roadmaps
- Scaled the platform and product portfolio by approximately 10× while keeping infrastructure growth under control
- Reduced AWS OpenSearch costs by approximately 50% through data lifecycle and storage architecture redesign
- Established CI/CD, automated testing, observability, and production operation standards
- Supported multiple IoT product lines involving telemetry, alerts, location tracking, video streaming, and device integration
- Designed RAG systems using dense and keyword-based hybrid retrieval
- Built multi-agent workflows with specialist agents and supervisor routing
- Developed evaluation and feedback loops for improving agent reliability
- Created reusable AI skills for engineering management and operational decision-making
- Integrated AI capabilities into backend and engineering workflows
- Leadership: Engineering Management, Technical Strategy, Delivery Management, Hiring, Mentoring, Stakeholder Management
- Backend: Node.js, TypeScript, Python, Golang
- Cloud & Infrastructure: AWS, GCP, Kubernetes, Docker, EKS, Pulumi, Helm
- Data & Messaging: PostgreSQL, Redis, Kafka, NSQ, OpenSearch, Milvus
- AI Engineering: RAG, Agent Systems, Vector Search, Hybrid Retrieval, Ollama, VoltAgent
- Observability: Grafana, Loki, Tempo, OpenTelemetry
- Architecture: Distributed Systems, Domain-Driven Architecture, Event-Driven Architecture, Multi-Tenant Platforms, Microservices
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Structural Pressure Model A socio-economic risk analysis model exploring housing burden, elite competition, labor stress, and AI-driven disruption across the United States and Taiwan.
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RAG Knowledge Base A domain-specific knowledge system built with retrieval-augmented generation.
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Claude Code Relay Development tooling for AI-assisted coding workflows.
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AHoy App A collaborative application project.