355 lines
16 KiB
JSON
355 lines
16 KiB
JSON
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{
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"en": {
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"header": {
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"title": "Rock Liang",
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"subtitle": "Senior Software Engineer",
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"nav": {
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"home": "Home",
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"askPoimen": "AskPoimen",
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"skills": "Skills",
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"contact": "Contact"
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},
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"experience": "Experience",
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"experienceValue": "6+ YoE",
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"education": "Education",
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"educationValue": "M.Sc. UOttawa",
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"educationDetails": {
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"bachelor": {
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"school": "University of Ottawa",
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"degree": "Bachelor's Degree, Computer Science",
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"period": "2016 – 2019"
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},
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"master": {
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"school": "University of Ottawa",
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"degree": "Master's degree, Computer Science",
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"period": "Sep 2019 – Nov 2021"
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}
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},
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"contact": {
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"portfolio": "portfolio.riotpiao.com",
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"github": "GitHub",
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"privateGithub": "Private Github",
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"linkedin": "LinkedIn",
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"email": "Email",
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"copy": "Copy"
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},
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"skillsPlaceholder": "Skills will appear here"
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},
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"hero": {
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"title": "Senior Software Engineer",
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"subtitle": "Infrastructure × Backend × LLM Systems",
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"description": "Building observable, scalable, fault-tolerant systems for mass audience.",
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"exploreMore": "Explore more",
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"terminalHint": "Press",
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"terminalHintSuffix": "to explore via terminal",
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"roles": [
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"Infrastructure",
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"SRE",
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"SDE",
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"Agentic Engineer",
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"Distributed Systems",
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"System/Platform Engineer"
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]
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},
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"bio": {
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"title": "About Me",
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"intro": "Senior Software Engineer with 6+ years of experience building observable, scalable, and fault-tolerant systems for mass audiences.",
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"expertise": {
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"title": "Technical Expertise",
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"content": "Infrastructure automation (Terraform, Kubernetes, ArgoCD) • Distributed systems (gRPC, Kafka, AWS Step Functions) • LLM inference optimization (INT4/INT8 quantization) • Backend systems (Go, Java, Python, C++)"
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},
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"highlights": {
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"title": "Career Highlights",
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"items": [
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"Launched AWS Distributed-Map service across 57+ regions",
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"Built multi-region infrastructure at RBC (40% cost reduction)",
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"Architected production homelab: Kubernetes, Kafka, LLM inference",
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"Open source: go-flink (distributed DataLakeHouse)"
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]
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},
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"educationTitle": "Education",
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"educationContent": "M.Sc. Computer Science, University of Ottawa (2019–2021)\nB.Sc. Computer Science, University of Ottawa (2016–2019)",
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"currentFocus": "Currently focused on infrastructure automation, LLM systems optimization, and building the next generation of distributed systems."
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},
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"projects": {
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"title": "What have i Built and Building",
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"subtitle": "Demonstrate my work",
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"watchVideo": "Watch Video",
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"readArticle": "Read Article",
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"askPoimen": "Ask Poimen for technical details",
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"items": [
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{
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"title": "AWS Distributed-Map",
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"description": "Launched distributed task orchestration service for mission-critical workloads.",
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"longDescription": "Optimized execution across 57+ regions with fault-tolerant scheduling and auto-scaling.",
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"stat": "Production launch, 57+ regions"
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},
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{
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"title": "RBC: Multi-Cloud Platform",
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"description": "Unified infrastructure platform consolidating public cloud and on-prem.",
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"longDescription": "Terraform automation with Temporal orchestration. 99.2% automated provisioning, notification-driven operator fallback.",
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"stat": "200+ microservices, 4 regions, 2hr→20min deploy",
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"highlight": "Standardized IaC patterns across 12 teams—cut provisioning toil, 3x integration velocity.",
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"bullets": [
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"Integrated Terraform Cloud to centralize IaC workflows—reduced onboarding time for 12 teams",
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"Built K8s CronJob to detect and reconcile Terraform state drift automatically (Golang)",
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"Designed Slack notification service with Golang workers—operators resolve apply failures in <5min",
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"Led requirement gathering across 4 platform teams—disambiguated specs, unblocked 3 stalled projects",
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"Translated technical decisions for non-technical stakeholders—secured buy-in for platform migration"
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],
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"deepDive": {
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"label": "Deep Dive: Terraform State Drift Solution →",
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"url": "/terraform-drift"
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}
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},
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{
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"title": "Homelab: Kubernetes Cluster",
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"description": "Bare-metal Kubernetes cluster running production workloads.",
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"longDescription": "Talos OS with 3-node control plane. Cilium eBPF CNI, Longhorn storage, ArgoCD GitOps.",
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"stat": "3-node cluster, 99.2% uptime"
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},
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{
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"title": "Homelab: LLM Inference",
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"description": "Production LLM inference pipeline with CPU optimization.",
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"longDescription": "Temporal-based orchestration with INT4/INT8 quantization. Achieved 60% latency reduction.",
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"stat": "60% latency cut, real-time inference"
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},
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{
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"title": "Homelab: Kafka Cluster",
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"description": "High-throughput event streaming with Strimzi KRaft.",
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"longDescription": "3-broker KRaft cluster with auto-scaling, persistent storage, and monitoring.",
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"stat": "3 brokers, 1K+ msgs/sec"
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},
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{
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"title": "Open Source: go-flink",
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"description": "Distributed DataLakeHouse framework written in Go.",
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"longDescription": "Fault-tolerant data pipelines with streaming semantics and efficient processing.",
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"stat": "Public repository, active"
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}
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]
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},
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"experience": {
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"title": "Explore Experience",
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"subtitle": "Feel free to Ask Poimen more abt it",
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"items": [
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{
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"company": "riotpiao.com",
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"role": "DevOps / SRE / SDE",
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"period": "May 2026 — Present",
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"description": "Built and maintain production-grade homelab on bare-metal Kubernetes (3-node Talos cluster). Architected Temporal-based LLM inference pipelines with 60% latency reduction. Full GitOps automation with ArgoCD, Terraform, and custom operators."
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},
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{
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"company": "RBC",
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"role": "Lead Software Engineer",
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"period": "Nov 2024 — May 2026",
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"description": "Deployments were manual and slow—teams blocked 2+ hours waiting. Chose Terraform for idempotent drift handling, Temporal for multi-cloud orchestration with built-in retry. Cut deploy time to 20min, consolidated public cloud and on-prem into single platform with 99.2% automation and notification-driven operator fallback."
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},
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{
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"company": "AWS",
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"role": "Senior Software Engineer (Step Functions)",
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"period": "2022 — 2024",
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"description": "Core contributor to AWS Step Functions, scaling to 57+ regions. Designed and implemented 8 critical components for distributed state machine execution. Achieved sub-100ms P99 latency for high-throughput workflow orchestration."
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},
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{
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"company": "Titus",
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"role": "ML Software Engineer Intern",
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"period": "May — Aug 2019",
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"description": "Built Personal Data Detection pipeline with 97.8% accuracy using transformer models. Developed Golang connector reducing p99 latency by 28%. Created SmartRegex system accelerating model deployment by 5x."
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},
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{
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"company": "NAV Canada",
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"role": "Summer Student",
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"period": "May — Aug 2018",
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"description": "Developed CFPS Flight Planning application using Django and React. Implemented NOTAMJ polling system and integrated Sonar code coverage. Built FWGS weather briefing interface for pilots."
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}
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]
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},
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"terminal": {
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"title": "Poimen (Agent Terminal)",
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"button": "Ask Poimen",
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"helpText": "type 'help' for commands"
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},
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"footer": {
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"copyright": "© 2025 Rock Liang. Deployed on homelab Kubernetes cluster (3-node Talos).",
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"github": "GitHub",
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"email": "Email"
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}
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},
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"zh": {
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"header": {
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"title": "梁伟哲",
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"subtitle": "高级软件工程师",
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"nav": {
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"home": "首页",
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"askPoimen": "问Poimen",
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"skills": "技能",
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"contact": "联系"
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},
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"experience": "经验",
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"experienceValue": "6年+",
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"education": "学历",
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"educationValue": "渥太华大学硕士",
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"educationDetails": {
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"bachelor": {
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"school": "渥太华大学",
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"degree": "计算机科学学士",
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"period": "2016 – 2019"
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},
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"master": {
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"school": "渥太华大学",
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"degree": "计算机科学硕士",
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"period": "2019年9月 – 2021年11月"
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}
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},
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"contact": {
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"portfolio": "portfolio.riotpiao.com",
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"github": "GitHub",
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"privateGithub": "私人GitHub",
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"linkedin": "领英",
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"email": "邮箱",
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"copy": "复制"
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},
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"skillsPlaceholder": "技能将显示在这里"
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},
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"hero": {
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"title": "高级软件工程师",
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"subtitle": "基础设施 × 后端 × LLM系统",
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"description": "为大规模用户构建可观测、可扩展、容错的系统。",
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"exploreMore": "了解更多",
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"terminalHint": "按",
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"terminalHintSuffix": "通过终端探索",
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"roles": [
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"基础设施",
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"SRE",
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"软件开发",
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"AI代理工程师",
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"分布式系统",
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"系统/平台工程师"
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]
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},
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"bio": {
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"title": "关于我",
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"intro": "拥有6年以上经验的高级软件工程师，专注于为大规模用户构建可观测、可扩展、容错的系统。",
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"expertise": {
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"title": "技术专长",
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"content": "基础设施自动化 (Terraform, Kubernetes, ArgoCD) • 分布式系统 (gRPC, Kafka, AWS Step Functions) • LLM推理优化 (INT4/INT8量化) • 后端系统 (Go, Java, Python, C++)"
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},
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"highlights": {
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"title": "职业亮点",
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"items": [
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"在57+区域发布AWS Distributed-Map服务",
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"在RBC构建多区域基础设施（成本降低40%）",
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"架构生产级家庭实验室：Kubernetes、Kafka、LLM推理",
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"开源项目：go-flink（分布式数据湖仓库）"
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]
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},
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"educationTitle": "教育背景",
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"educationContent": "渥太华大学计算机科学硕士 (2019–2021)\n渥太华大学计算机科学学士 (2016–2019)",
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"currentFocus": "目前专注于基础设施自动化、LLM系统优化，以及构建下一代分布式系统。"
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},
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"projects": {
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"title": "我构建的项目",
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"subtitle": "展示我的作品",
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"watchVideo": "观看视频",
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"readArticle": "阅读文章",
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"askPoimen": "问Poimen了解技术细节",
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"items": [
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{
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"title": "AWS Distributed-Map",
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"description": "发布面向关键任务的分布式任务编排服务。",
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"longDescription": "在57+区域优化执行，具有容错调度和自动扩展功能。",
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"stat": "生产发布，57+区域"
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},
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{
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"title": "RBC: 多云平台",
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"description": "统一基础设施平台，整合公有云和本地部署。",
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"longDescription": "Terraform自动化配合Temporal编排。99.2%自动化配置，通知驱动的运维人员兜底。",
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"stat": "200+微服务，4个区域，部署2小时→20分钟",
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"highlight": "标准化12个团队的IaC模式——减少配置负担，集成速度提升3倍。",
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"bullets": [
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"集成Terraform Cloud实现IaC工作流集中化——12个团队onboarding时间缩短",
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"构建K8s CronJob自动检测和修复Terraform状态漂移（Golang）",
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"设计Slack通知服务 + Golang worker——运维人员<5分钟解决apply失败",
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"主导4个平台团队需求收集——澄清规格，解锁3个停滞项目",
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"向非技术stakeholder翻译技术决策——获得平台迁移支持"
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],
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"deepDive": {
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"label": "深入了解：Terraform状态漂移解决方案 →",
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"url": "/terraform-drift"
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}
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},
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{
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"title": "家庭实验室：Kubernetes集群",
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"description": "运行生产工作负载的裸机Kubernetes集群。",
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"longDescription": "Talos OS 3节点控制平面，Cilium eBPF CNI，Longhorn存储，ArgoCD GitOps。",
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"stat": "3节点集群，99.2%可用性"
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},
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{
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"title": "家庭实验室：LLM推理",
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"description": "具有CPU优化的生产LLM推理管道。",
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"longDescription": "基于Temporal的编排，INT4/INT8量化，延迟降低60%。",
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"stat": "延迟降低60%，实时推理"
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},
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{
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"title": "家庭实验室：Kafka集群",
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"description": "使用Strimzi KRaft的高吞吐量事件流。",
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"longDescription": "3代理KRaft集群，具有自动扩展、持久存储和监控。",
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"stat": "3代理，1K+消息/秒"
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},
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{
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"title": "开源：go-flink",
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"description": "用Go编写的分布式数据湖仓库框架。",
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|
|
"longDescription": "具有流语义和高效处理的容错数据管道。",
|
|||
|
|
"stat": "公开仓库，活跃中"
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"experience": {
|
|||
|
|
"title": "工作经历",
|
|||
|
|
"subtitle": "欢迎向Poimen了解更多",
|
|||
|
|
"items": [
|
|||
|
|
{
|
|||
|
|
"company": "riotpiao.com",
|
|||
|
|
"role": "DevOps / SRE / 软件开发",
|
|||
|
|
"period": "2026年5月 — 至今",
|
|||
|
|
"description": "在裸机Kubernetes（3节点Talos集群）上构建和维护生产级家庭实验室。架构基于Temporal的LLM推理管道，延迟降低60%。使用ArgoCD、Terraform和自定义操作器实现完整GitOps自动化。"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"company": "RBC",
|
|||
|
|
"role": "技术主管",
|
|||
|
|
"period": "2024年11月 — 2026年5月",
|
|||
|
|
"description": "部署流程手动且缓慢——团队等待2+小时。选择Terraform处理幂等漂移，Temporal用于多云编排和内置重试。将部署时间缩短至20分钟，整合公有云和本地部署为统一平台，99.2%自动化，通知驱动的运维人员兜底机制。"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"company": "AWS",
|
|||
|
|
"role": "高级软件工程师 (Step Functions)",
|
|||
|
|
"period": "2022 — 2024",
|
|||
|
|
"description": "AWS Step Functions核心贡献者，扩展至57+区域。设计实现8个关键组件用于分布式状态机执行。高吞吐量工作流编排实现P99延迟低于100ms。"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"company": "Titus",
|
|||
|
|
"role": "机器学习软件工程师实习",
|
|||
|
|
"period": "2019年5月 — 8月",
|
|||
|
|
"description": "使用transformer模型构建准确率97.8%的个人数据检测管道。开发Golang连接器，p99延迟降低28%。创建SmartRegex系统，模型部署速度提升5倍。"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"company": "NAV Canada",
|
|||
|
|
"role": "暑期实习生",
|
|||
|
|
"period": "2018年5月 — 8月",
|
|||
|
|
"description": "使用Django和React开发CFPS航班计划应用。实现NOTAMJ轮询系统并集成Sonar代码覆盖。为飞行员构建FWGS天气简报界面。"
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"terminal": {
|
|||
|
|
"title": "Poimen（代理终端）",
|
|||
|
|
"button": "问Poimen",
|
|||
|
|
"helpText": "输入 'help' 查看命令"
|
|||
|
|
},
|
|||
|
|
"footer": {
|
|||
|
|
"copyright": "© 2025 梁伟哲。部署于家庭实验室Kubernetes集群（3节点Talos）。",
|
|||
|
|
"github": "GitHub",
|
|||
|
|
"email": "邮箱"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|