Chee Lim - AI Engineer at Self-employed

Chee Lim

AI Engineer at Self-employed

Kuala Lumpur, Malaysia

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Summary

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Senior

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Top School

Chee Lim is an AI Engineer and Principal Software Architect based in Kuala Lumpur with 15 years of experience designing and delivering enterprise-grade and self-hosted AI systems. He blends deep Java and cloud-native engineering from past roles at DXC and Zurich with modern TypeScript, Docker, and Python tooling to bridge legacy data stores and privacy-first AI agents. As an independent engineer he launched Project Concord and RAG.WTF, built open-text-embeddings to cut embedding costs by up to 70–90%, and operationalized local AI stacks running multiple LLMs and real-time voice pipelines. He pairs pragmatic infrastructure choices (Coolify, TensorDock, Modal) with hands-on model orchestration (Ollama, LocalAI, llama.cpp) to deliver resilient, cost-efficient AI deployments. Known for shipping production analytics and conversational AI in large organizations, he also actively contributes open-source software that prioritizes data ownership and vendor independence. Chee’s uncommon combination of enterprise delivery experience and experimental self-hosted AI tooling makes him effective at modernizing legacy systems for secure, low-latency AI use.

15 years of coding experience

11 years of employment as a software developer

Bsc (Hons) Degree in Computing Second Upper Computer Science, Bsc (Hons) Degree in Computing Second Upper Computer Science at University of Staffordshire

Asia Pacific Institute of Information Technology (APIIT)

Github Skills (200)

api10

text-generation10

mamba10

materialize10

datatables10

flutter-widget10

walletconnect10

checkbox10

gateway10

bootstrap-table9

intellij9

llm9

css9

alarmmanager9

llama9

Programming languages (14)

MDXPowerShellJavaC++CSSRustGoJupyter Notebook

Github contributions (5)

limcheekin/activiti-spring-security

Jan 2012 - Jun 2013

Contributions:8 commits in 1 year 4 months

spring-bootsecurityactivitispringjava

limcheekin/serverless-ml

Mar 2021 - Jul 2024

Deploy Serverless Machine Learning Models to AWS Lambda

Contributions:370 pushes, 48 branches in 3 years 4 months

aws-lambdaserverlessmachine-learningmlopsaws

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