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Open to opportunities · Hong Kong

Ziyun Liao

Forward-Deployed AI Engineer·Full-Stack & Agents

> building Multi-Agent Systems

I turn ambiguous real-world problems into deployed, production-grade AI products — from multi-agent orchestration and RAG pipelines to Dockerized delivery. Sole architect of a contract-risk platform running on real Hong Kong government tenders.

Hong Kong · limibo023@gmail.com

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Multi-Agent Systems//RAG Pipelines//LLM Orchestration//Adversarial Debate//Qdrant Vector Search//Contract Risk AI//FastAPI//Docker//Next.js//MCP//Prompt Engineering//Production AI//Multi-Agent Systems//RAG Pipelines//LLM Orchestration//Adversarial Debate//Qdrant Vector Search//Contract Risk AI//FastAPI//Docker//Next.js//MCP//Prompt Engineering//Production AI//

[01] // about

Problem in, product out

Full-stack AI engineer with a forward-deployed (FDE) mindset, turning ambiguous real-world problems into deployed, production-grade AI products. Specialized in agent development — multi-agent LLM orchestration, RAG, and tool/pipeline design — and end-to-end delivery across Python backends, vector search, and containerized deployment, on a solid deep-learning and computer-vision foundation.

My edge is the combination: a research-grade deep-learning foundation (two papers under review) and the engineering discipline to ship — scoping with stakeholders, designing agent architectures, and owning the stack all the way to Dockerized deployment with tests.

// currently: AI Engineer, Office of the CEO @ IsBIM Limited, Hong Kong

2+

Years shipping AI systems end-to-end

4

Agent systems designed & shipped

days→hours

Senior-reviewer workload automated

2

Papers (1 in Pattern Recognition)

[02] // spotlight

Flagship: shipped to production

[01] Compliance Review

Every tender requirement verified against the standards clause library

→ Compliance Risk Register

[02] Consistency Check

Cross-document contradictions pinned to the exact conflicting clauses

→ Document Consistency Review

[03] Risk Analysis

Deep NEC4 / GCC contractual exposure scoring & adjudication

→ Contractual Risk Review

Parsing & indexing core

parse · deterministic-first

  • The parser is treated as an untrusted component: every parse is audited against the source PDF's own text layer, then repaired — or refused — before anything downstream sees it
  • Every page's provenance is recorded — native text layer vs image-derived — so downstream stages decline to reason over content the parser only guessed at

index · hybrid + pre-computed

  • Chunking follows the document's own clause-and-section grammar, so a retrieval hit lands on a citable contractual unit, not an arbitrary span sharing vocabulary
  • Vectors are deliberately not the only index: typed facts (amounts, dates, cross-references) and structural anchors are extracted deterministically at index time — removing an entire class of wrong-but-plausible evidence

retrieve · evidence-first grounding

  • Retrieval is a recall mechanism, never an arbiter: nearest neighbors always return something, so structural evidence outranks similarity hits — no finding is decided on similarity alone
  • Lexical and semantic signals are fused and re-ordered before any LLM sees the evidence — a choice driven by measurement: on real corpora, raw scores and misleading evidence overlapped completely, so no cutoff could separate them

Review pipeline — live flow

IntakeIndexComplianceConsistencyRiskAdjudicateDeliver

➜agent.shell — guarded LLM operator · SKILL.md domain packs · order / write / budget guards

400+

tender documents parsed

26

government tenders onboarded

3,800+

clauses indexed in Qdrant

540

checks run per tender

5

risk reports per tender

3

HK gov departments served

//servingCEDD (Civil Engineering & Development)·EPD (Environmental Protection)·HyD (Highways)

PythonQwen / DashScopeQdrantLlamaIndexOpenAI SDKFastAPIPyMuPDF / MinerUpython-docxElectronSSEPytestDocker

Parsing & indexing core

geometry · 360° → 12-pinhole rig

  • Equirect panoramas reprojected into 12 rigid pinhole views (4 yaw × 3 pitch) so standard SfM applies — with fixed rig extrinsics massively constraining the solution
  • Geometric masks exclude the operator, zenith/nadir and seams from ever polluting features

features · learned over handcrafted

  • DISK learned features with LightGlue matching — robust on the low-texture, repetitive surfaces of construction sites where SIFT gives up
  • Sequential windows plus loop-closure pairing, geometrically verified before entering the map

solver · GPU bundle adjustment

  • Incremental SfM with cuDSS sparse-direct bundle adjustment on GPU (~2.3×) — intrinsics and rig extrinsics held fixed throughout
  • Continuation pass recovers unregistered frames instead of silently dropping them

contract · sim2 to plan space

  • Two field-anchored points solve scale, rotation and translation from dimensionless SfM space into real floor-plan pixels — with mirror-flip detection
  • Every hotpoint ships with heading, in-frame ratio and a confidence gate, not just coordinates

Localization pipeline — live flow

Frames12-PinholeMaskDISK+GluecuDSS SfMsim2 AlignHotpoints

12

pinhole rig views per frame

1,045

frames per walkthrough

2.3×

faster BA on cuDSS GPU

≈100%

in-frame on good runs

80%

fallback trigger threshold

2

main + fallback pipelines

PythonCOLMAP / cuDSSDISKLightGlueStella-VSLAMPyTorchffmpegDocker

Parsing & indexing core

state machine · lesson as backbone

  • The teaching loop is modeled as an explicit state machine — scheduled → live → organizing → publish → grading → completed — with tools and permissions gated per phase
  • AI suggests, teacher finalizes: every AI artifact (slides, summaries, grades) stays a draft until a human ships it

agent boundary · MCP orchestration

  • PPT generation runs as a design-first agent over stdio MCP, rendered asynchronously in the background while the teacher keeps working — then edited page by page with the agent
  • Tool calls are constrained by whitelist and phase — the agent can only act inside the current lesson state

delivery · desktop-first, web-ready

  • Electron with strict IPC isolation: the renderer never touches keys, paths or processes — model configuration stays in the main process
  • TypeScript monorepo on a Supabase-aligned schema, so the persistence swap is a migration, not a rewrite

orchestration · multi-provider LLM routing

  • Admin-managed provider catalog with model discovery and per-deployment defaults — providers swap without touching product code
  • A constraint-solving timetable engine negotiates coverage across 80 teachers × 24 classes under hard constraints

Lesson lifecycle — live flow

PrepareLiveOrganizePublishGradeComplete

6

lesson states, machine-driven

3

roles — teacher / student / admin

80×24

teacher-class scheduling scale

live

in-class slide sync for students

3

interface languages

stdio MCP

agent boundary

TypeScriptElectronReactNode.jsMCPFastAPISupabaseDeepSeek / OpenAI

[03] // projects

Open source work

Onyx

AI-Native Team Collaboration Platform

Hand the AI a goal and it decomposes it into assignable kanban tasks for human review and sign-off. Each user gets a personal AI assistant with 26 tools, similarity-based tool search, and parallel sub-agent spawning — plus project wiki, sprint cycles, real-time SSE notifications, and an MCP server that lets local agents report structured work. Deployed for internal team demo.

26

assistant tools

3

concurrent sub-agents

5

integrations

3

languages i18n

PythonFastAPIPostgreSQLNode.jsDeepSeekMCPSSEDocker

Indoor Navigation App

WiFi & BLE positioning on Android

Indoor navigation Android application built from scratch, integrating WiFi & BLE positioning with A-star pathfinding for real-time route planning.

KotlinJavaWiFi RTTBLEA*

//more experiments & research code on github.com/Aslimbo

[04] // skills

The capability stack

~/skills.sys3 layers · 27 entries

[L1]

AI Agents & LLM

Core specialty

Multi-Agent Pipeline DesignLLM OrchestrationAgent Goal & Tool DesignRAGEvidence-Grounded QAMCPTool / Function CallingCustom Agent DevelopmentMulti-Provider LLM RoutingVector Search (Qdrant)

[L2]

Full-Stack & Deployment

End-to-end delivery

PythonFastAPI / FlaskReact / Next.jsTypeScript MonorepoElectron Desktop AppsAsync / Concurrent PipelinesPostgreSQLSSE Real-Time SystemsDockerQuality-Gated Delivery

[L3]

ML & Computer Vision

Research foundation

PyTorchOpenCVStructure-from-MotionVisual SLAMLearned Feature MatchingPose Estimation & SAR ATRGeometric Alignment (sim2)

core — daily drivers; the rest are working proficiency

languages.code=[Python, C++, C, Java, Kotlin, Rust]

languages.spoken=[English (IELTS 7.0), Chinese]

[05] // experience

Track record

AI Engineer — Office of the CEO

current

IsBIM Limited·Hong Kong·Sept 2025 — Present

Sole engineer across two production lines at IsBIM: contract-risk intelligence and 360° spatial localization.

Tender Agent — AI Contract-Risk PlatformFeb 2026 — Present

  • Scoped the problem directly with firm leadership and real government tenders, then designed, built, and shipped a multi-phase AI pipeline for automated compliance & risk analysis of HK construction tenders (NEC4, GCC) — 228 modules / 94K+ lines of Python, 239 tests, in production.
  • Designed the multi-agent reasoning core that pits contractor-side against employer-side arguments to surface hidden contractual risk — 83% accuracy on genuine risk exposures, with every finding grounded in retrieved clauses.
  • Built the agentic layer: a single-loop LLM agent with coarse-grained guarded tools (order / write / budget breakers), SKILL.md domain packs, an SSE web UI and an Electron desktop app — orchestrating the deterministic pipeline it never replaces.
  • Engineered the retrieval + evidence core on Qdrant (3,800+ indexed clauses): typed claim indexing, an anchor registry, and structural evidence pinning that structurally killed false-positive judgments — precision 0.27 → 0.71 on real tenders.
  • Owned the full stack: FastAPI search services, concurrent LLM execution with deterministic fallbacks, PyMuPDF/MinerU PDF ingestion, python-docx risk-classified reporting, Dockerized deployment — plus a multi-AI-agent dev workflow (Claude architect, Codex reviewer, Gemini brainstormer) under quality-gated peer review.

Eagle Eye — 360° Walkthrough Localization2025.09 — 2026.02

  • Engineered the offline localization pipeline: 12-pinhole rig reprojection of equirect panoramas, DISK/LightGlue learned feature matching with geometric masking, and cuDSS GPU-accelerated incremental SfM with continuation recovery.
  • Owned the real-time + fallback architecture: a Stella-VSLAM live link with automatic offline COLMAP fallback below 80% registration, and sim2 plan-space alignment from dimensionless SfM space to floor-plan pixels — mirror-flip detection and confidence gating included.
  • Delivered the product contract: position, heading, in-frame ratio and confidence per panorama — ≈100% in-frame on good runs, powering the clickable 360 walkthrough on floor plans.

Knowledge Base — Enterprise RAG & Agentic Chatbot2025.05 — 2026.06

  • Architected a dual-path enterprise knowledge base: a fully self-hosted local RAG stack for sensitive material, plus a managed path that syncs documents to NotebookLM for curation and Q&A — routed by confidentiality tier.
  • Designed the agent layer on Pi Agent Core from scratch: a custom tool set, tool-use routing and goal-validation loops built for knowledge work rather than framework defaults — the same agent core later reused across internal tooling.
  • Engineered grounded QA over the curated corpus: structure-aligned chunking, hybrid retrieval, and citations back to source pages — the agent answers 'unknown' on thin evidence instead of hallucinating.

Research Assistant

Centre for Intelligent Multidimensional Data Analysis Limited·Hong Kong·Feb 2024 — Aug 2025

Applied research across interpretable deep learning, NLP, and multi-sensor positioning.

  • Interpretable Deep SAR ATR: hybrid CNN–ViT–MobileNet architecture with two-stage physics-based feature decomposition — 60% fewer parameters, +12% accuracy on MSTAR.
  • Semantic sentiment analysis: multi-task BERT/RoBERTa pipeline with phased fine-tuning and Bayesian hyperparameter optimization, cutting search space by 50%.
  • Multi-sensor indoor positioning (WiFi & BLE & UWB): PSO-calibrated propagation models plus UWB-AoA + Kalman fusion — 0.5 m average accuracy in dynamic indoor scenarios.
  • PowerSkel++: Conformer-based multi-task WiFi CSI pose estimation for 10 kV substations — 92.08% PCK@50, robust under sensor/sample degradation.

education

The University of Hong Kong

MSc in Electrical and Electronic Engineering

Sept 2022 — July 2023

Tongji University

BE in Electrical Engineering and Automation

Sept 2015 — July 2019

▼

[06] // publications

Research output

Preprint

An Interpretable Two-Stage Feature Decomposition Method for Deep Learning-based SAR ATR

Chenwei Wang, Renjie Xu, Congwen Wu, Cunyi Yin, Ziyun Liao, Deqing Mao, Sitong Zhang, Hong Yan

→ arXiv:2506.09377

Jun 2025

Accepted

PowerSkel++: A Robust WiFi CSI-based Pose Estimation Framework for Power Stations via Collaborative Learning under Signal Degradation

Cunyi Yin, Chenwei Wang, Zhaoke Huang, Jing Chen, Ziyun Liao, Zhenghua Chen, Zhiwen Chen, Chuan Huang, Hong Yan

→ Pattern Recognition

07 / contact

Let's ship the answer.

Ambiguity in, production out — agent architectures, retrieval pipelines, and the full-stack engineering to ship them.

© 2026 Ziyun Liao·designed & built from scratch

next.js + tailwind + gsap