{"id":1429,"date":"2026-08-18T18:01:29","date_gmt":"2026-08-18T10:01:29","guid":{"rendered":"https:\/\/www.rayer.idv.tw\/blog\/?p=1429"},"modified":"2026-08-18T18:01:31","modified_gmt":"2026-08-18T10:01:31","slug":"professional-profile","status":"publish","type":"page","link":"https:\/\/www.rayer.idv.tw\/blog\/?page_id=1429","title":{"rendered":"Professional Profile"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">At the very beginning of the text , My current resume is available <a href=\"https:\/\/www.rayer.idv.tw\/blog\/wp-content\/uploads\/2026\/08\/RayerTung_Resume_Staff_Backend_Platform_v2.pdf\">here<\/a>.<\/p>\n\n\n\n\n<p class=\"wp-block-paragraph\">I am Rayer Tung, an AI-native Staff Backend and Platform Engineer working primarily with Go, Java, and Rust. I focus on turning closed legacy systems and manual workflows into programmable, scalable, and reliable production platforms. I keep a hands-on scope across architecture, implementation, migration and rollout, production incident work, and multi-team adoption.<\/p>\n\n\n\n\n<h2 class=\"wp-block-heading\">Career highlights<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n\t<li><strong>AI-native engineering :<\/strong> Led a department-selected proof of concept on enterprise AI knowledge systems and incident workflow exploration. The work stayed source-grounded and focused on inspection and trust, with incident triage and remediation remaining human-reviewed.<\/li>\n\t<li><strong>Legacy CORBA compatibility platform:<\/strong> At TSMC I converted nearly 1,000 CORBA IDL operations into HTTP endpoints, OpenAPI specs, and Go\/Java SDKs. This closed integration gap for consumers while maintaining the legacy compatibility boundary, and it became a production integration path adopted by three internal teams.<\/li>\n\t<li><strong>Automated migration platform for production VMs:<\/strong> Built and shipped an automated rolling migration for 1,400+ production VMs. A typical 6\u20137-node migration moved from about two engineer-days to roughly 1\u20132 largely unattended hours, with more than 99% first-run success and automatic retry handling for transient failures.<\/li>\n\t<li><strong>Experiment integration modernization:<\/strong> At Binance I owned the RFC, API contract, server integration layer, and Go\/Java\/Rust SDKs that turned a web-only A\/B testing integration path into a programmable platform. The platform handled about 40K QPS and was adopted by 7+ business units.<\/li>\n\t<li><strong>High-scale recommendation and serving:<\/strong> At Appier I owned a recommendation and advertising serving platform at 20K\u201360K QPS and re-architected core serving behavior into Go on GKE. Matched-period billing comparisons showed an estimated 65% reduction in product-attributed cloud costs.<br><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">LLM Wiki Cloud<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">I am building LLM Wiki Cloud end to end as an AI knowledge workspace. It turns Markdown and web sources into structured, searchable, source-linked knowledge and coherent topic-level entries.<\/p>\n\n\n\n\n<p class=\"wp-block-paragraph\">In enterprise knowledge workflows, fragment-level RAG often retrieves passages without a coherent system overview. I built concept-oriented compilation across documents so teams get organized topics with source traceability.<\/p>\n\n\n\n\n<p class=\"wp-block-paragraph\">I own this end-to-end: product scope, architecture decisions, and implementation.<\/p>\n\n\n\n\n<ul class=\"wp-block-list\">\n\t<li><strong>Multi-tenancy and tenant isolation<\/strong><\/li>\n\t<li><strong>BYOK<\/strong><\/li>\n\t<li><strong>Markdown and web ingest<\/strong><\/li>\n\t<li><strong>Ingest, compile, and publish workflow<\/strong><\/li>\n\t<li><strong>Structured knowledge generation and search<\/strong><\/li>\n\t<li><strong>Retrieval with source traceability<\/strong><\/li>\n\t<li><strong>Publicly browsable demo content<\/strong><br><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/wiki.rayer.idv.tw\/\">Try the public demo<\/a> (choose <strong>Try Demo<\/strong> on the sign-in screen).<\/p>\n\n\n\n\n<p class=\"wp-block-paragraph\">The aim is not merely answer generation. The goal is knowledge outputs people can understand, verify, and trust.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>At the very beginning of the text , My current resume is available here. I am Rayer Tung, an AI-native Staff Backend and Platform Engineer working primarily with Go, Java, and Rust. I focus on turning closed legacy systems and manual workflows into programmable, scalable, and reliable production platforms. I keep a hands-on scope across architecture, implementation, migration and rollout, production incident work, and multi-team adoption. Career highlights AI-native engineering : Led a departmen &hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-1429","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Professional Profile - Fox Nest<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.rayer.idv.tw\/blog\/?page_id=1429\" \/>\n<meta property=\"og:locale\" content=\"zh_TW\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Professional Profile - Fox Nest\" \/>\n<meta property=\"og:description\" content=\"At the very beginning of the text , My current resume is available here. 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