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Current CGPA / 4.00
Graduate AI Engineer Candidate
AI Systems.
Quant Logic.
Clean Interfaces.
Final-year Computer Science student specializing in Artificial Intelligence and Data Science, with enterprise IT operations experience at OCBC Bank Headquarters and a track record of building full-stack AI-powered systems. I build practical AI products that connect machine learning, data pipelines, backtesting discipline, and human-readable interfaces.
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Dean's List Semester GPA (February 2026)
0+
Cloud, AI, Data & Oracle Certifications
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Expected Graduation Year (October 2026)
01 / About Me
Applied AI builder with product mindset.
A concise profile of my technical direction, project strengths, and the engineering mindset I bring to applied AI, data systems, and production-aware software development.
Professional Profile
Liew Zhen Chuan
Graduate AI Engineer Candidate · Applied AI · Data Science · Full-Stack Systems
My Introduction
Hi, I'm Liew Zhen Chuan.
I am a Bachelor of Computer Science (Honours) student at Universiti Tunku Abdul Rahman, focused on Artificial Intelligence, Data Science, and full-stack software engineering. My work sits at the intersection of machine learning, financial technology, backtesting systems, and user-facing analytical interfaces.
My strongest current project is QuantForge Pro, an AI-assisted stock and cryptocurrency analytical platform designed to help retail users move from market observation to more disciplined strategy validation. I care about building systems that are not only visually polished, but also explainable, testable, and honest about execution assumptions.
As a graduate AI engineer candidate, I want to contribute to teams building practical AI products, data-driven decision systems, LLM-integrated workflows, and production-aware software platforms. I bring a mix of AI learning, hands-on project execution, enterprise IT exposure, and a strong habit of improving technical quality step by step.
Focus AreaApplied AI, ML systems, backtesting, data products, and technical dashboards.
Engineering StyleClean UI, modular architecture, auditability, readable evidence, and disciplined testing.
Career TargetGraduate AI Engineer, Data Scientist, or Software Engineer roles.
Portfolio AngleShow practical product-building ability, not only coursework or certificates.
02 / Professional Signal
Enterprise-ready mindset.
My background combines AI engineering coursework, final-year product architecture, enterprise IT support, retail customer engagement, and hands-on operations work. That mix matters: AI engineers need more than models. They need secure systems, reliable data paths, clear debugging habits, user empathy, and responsible deployment thinking.
2026
UTAR Final Year Project
UTAR Final Year Project
images/quantforge-logo.pngLead Software Developer & Architect · QuantForge Pro
Designed and built an AI-assisted quantitative backtesting platform that connects historical market data, strategy configuration, sandbox simulation, audit verification, and comparative run management. The project focuses on realistic backtesting discipline, including close-of-bar decision logic, next-bar-open execution, slippage and commission awareness, risk metrics, and interpretable result evidence.
React
TypeScript
IndexedDB
Web Workers
AI APIs
Oct 2025 — Jan 2026
OCBC Bank HQ
OCBC Bank HQ
images/ocbc-logo.pngIT Support Engineer Intern · CTC Global / OCBC Bank Headquarters
Delivered Tier 1 and Tier 2 troubleshooting across hardware, software, network, domain integration, and endpoint deployments. Supported high-availability banking operations, handled Network Access Control through ForeScout, and contributed to IT asset audit analytics using Microsoft Power Apps and Excel dashboards.
Enterprise IT
NAC / ForeScout
Data Dashboards
Operations Reliability
Dec 2024 — Feb 2025
YongSheng Gift Shop
YongSheng Gift Shop
images/yongsheng-logo.pngProduct Promoter · YongSheng Gift Shop
Promoted signature gift and retail products in a fast-paced customer-facing environment. Engaged walk-in customers, explained product features clearly, supported daily sales targets, and helped maintain organized product displays, stock visibility, and a welcoming shopping experience during peak commercial periods.
Retail Sales
Customer Engagement
Product Presentation
Store Operations
Dec 2023 — Feb 2024
Biogreen Organic
Biogreen Organic
images/biogreen-logo.pngProduct Promoter · Biogreen Organic
Consulted retail customers on organic health products by understanding basic dietary needs, explaining product benefits, and recommending suitable options. Supported product education, customer trust building, inventory monitoring, stock replenishment, and well-organized in-store product displays.
Consultative Selling
Product Education
Health Products
Inventory Support
Apprentice · Best Truss Steel Roofing System
Operated and maintained manual milling machines for precision metal fabrication. Assisted with technical drawing interpretation and translation into machining operations, developing attention to detail, safety awareness, and a practical understanding of manual manufacturing workflows.
Manufacturing Support
Manual Milling
Technical Drawing
Precision Work
03 / Featured Build
From trading idea to audited evidence.
QuantForge Pro is my strongest technical project because it forces software engineering, AI integration, financial data discipline, and product design to work together. The design goal is simple: help users avoid blind prediction and evaluate strategy quality through evidence.
Flagship Project
QuantForge Pro — AI-Assisted Stock & Cryptocurrency Backtesting Platform
A cross-platform analytical system for retail investors and beginner traders. It integrates historical market data retrieval, feature preparation, AI-assisted sentiment and contextual support, strategy configuration, sandbox backtesting, audit-aware verification, and research workflow persistence.
Data SyncHistorical candles, range coverage, caching, validation.
Feature EnginePrecomputed indicators and execution-ready slices.
AI ContextMarket sentiment and narrative support through AI APIs.
Execution KernelBar-by-bar signals, order simulation, fills, equity updates.
Audit LayerTrade ledger reconstruction, sanity checks, integrity checks.
Research UXMetrics, charts, run registry, comparison matrix.
Look-ahead Control
Sharpe Ratio
Maximum Drawdown
Slippage / Fees
Run Comparison
System Principle
Prediction is not enough.
The platform treats forecasting as one input inside a broader workflow of execution simulation, risk evaluation, verification, and result interpretation.
Execution Contract
Close-of-bar decision, next-bar-open execution.
Signals are generated using available bar information and executed later, reducing ambiguity and strengthening backtest validity.
Second Project
Employee Salary Prediction System
Built classification models using Random Forest and XGBoost with feature engineering and evaluation through F1 Score, Precision, and Recall.
04 / Demo Evidence
Project evidence and system walkthrough.
Selected interface evidence from my technical projects, showing how the systems support data analysis, model evaluation, backtesting workflow, and user-facing decision support.
QuantForge Pro Demo Gallery
5 system views
Main DashboardAnalytical workspace with charting, strategy controls, and AI-assisted context.
Main Analytical Dashboard
Shows the full analytical workspace, including charting, strategy context, configuration controls, and technical visual hierarchy.
AI-Assisted Market SentimentSentiment console with market bias, confidence, volatility risk, live news feed, macro context, and AI availability status.
AI-Assisted Market Sentiment
Shows the sentiment intelligence layer: market news is converted into bullish, bearish, or neutral signals, then summarized into market bias, confidence, risk state, and macro context. The panel also keeps the AI advisory status transparent when the provider is unavailable because of quota or rate limits.
Performance MetricsRisk and result telemetry for disciplined strategy evaluation.
Performance Metrics
Presents key risk and performance indicators such as return, drawdown, win rate, Sharpe Ratio, and trade outcome quality.
Trade EvidenceOrder-level trade ledger, simulated fills, and audit trail.
Trade Evidence
Demonstrates order-level evidence, trade reconstruction, simulated fills, and the audit trail behind each backtest result.
Research WorkflowSaved runs, comparison matrix, and AI-assisted interpretation.
Research Workflow
Shows saved experiments, run comparison, research workflow continuity, and AI-assisted interpretation surfaces.
Employee Salary Prediction Gallery
2 ML views
Model WorkflowDataset preparation, feature engineering, and training pipeline.
Model Workflow
Shows dataset preparation, feature engineering, model training flow, and the structure of the machine-learning workflow.
Evaluation ResultsModel comparison, metrics, confusion matrix, and prediction output.
Evaluation Results
Presents classification results, model comparison, F1 Score, Precision, Recall, confusion matrix, and final prediction output.
05 / Technical Stack
AI plus software architecture.
I am strongest where machine learning meets implementation: data preparation, model evaluation, API integration, browser-side performance, and clean product interfaces. My current direction is to grow into an AI engineer who can ship reliable, explainable, production-aware systems.
Core Engineering Profile
- Programming: Python, TypeScript, SQL, Java, C++, HTML.
- Machine learning: Pandas, NumPy, Matplotlib, Scikit-learn, PyTorch.
- LLM and AI: OpenAI API, Google Gemini API, local LLMs, prompt engineering, Oracle Generative AI.
- Web architecture: React, IndexedDB, Web Workers, modular frontend design.
- Cloud and databases: Oracle Cloud Infrastructure, AWS Cloud Foundations, MySQL, Oracle Database.
Skill Map
Relative strengths based on current project work, coursework, internship exposure, and certifications.
Python / ML
TypeScript / React
SQL / Data
LLM Integration
Cloud Foundations
System Design
06 / Education & Credentials
Academic base with applied proof.
My academic path is focused on AI, data science, machine learning, deep learning, database systems, cloud computing, and software engineering fundamentals. The certifications reinforce that direction with vendor-backed AI, cloud, data, and database foundations.
07 / Certification Gallery
Verified certifications and learning proof.
Certification evidence supporting my AI, data science, cloud, database, and software development foundation across Oracle and AWS learning paths.
Oracle Generative AICertification evidence in generative AI foundations and AI solution awareness.
Oracle Generative AI
Validates applied understanding of generative AI concepts, prompt-driven workflows, and AI solution awareness.
Oracle Data ScienceCertification evidence in data science workflow and analytical model development.
Oracle Data Science
Supports my foundation in data science workflow, model development concepts, and analytical problem solving.
Oracle AI Vector SearchCertification evidence in vector search and AI-ready data retrieval.
Oracle AI Vector Search
Shows exposure to vector-based retrieval concepts, semantic search, and AI-ready database capabilities.
Oracle APEX Cloud DeveloperCertification evidence in cloud application development fundamentals.
Oracle APEX Cloud Developer
Demonstrates low-code cloud application development awareness and database-backed application delivery.
Oracle AI FoundationsCertification evidence in AI concepts, applications, and responsible use.
Oracle AI Foundations
Strengthens my baseline understanding of AI concepts, use cases, and responsible implementation considerations.
Oracle Data Platform FoundationsCertification evidence in database, cloud data, and platform fundamentals.
Oracle Data Platform Foundations
Covers modern data platform fundamentals, database services, and cloud-based data management concepts.
AWS Academy Cloud FoundationsCertification evidence in cloud infrastructure and service fundamentals.
AWS Academy Cloud Foundations
Covers cloud computing fundamentals, infrastructure concepts, and service-level awareness for modern application deployment.
08 / Contact
Let's build AI systems that are useful, testable, and honest.
I am seeking Graduate AI Engineer, Data Scientist, or Software Engineer opportunities where I can work on applied AI, data systems, LLM integration, and production-grade software architecture.
What I bring to a graduate AI team
- Ability to connect model outputs with practical product workflows instead of treating AI as a standalone feature.
- Strong bias toward verifiable systems: logs, audit trails, metric consistency, and clear user-facing evidence.
- Experience working with enterprise IT constraints, security awareness, and operational reliability.
- Frontend engineering taste: minimal visual hierarchy, clear dashboards, responsive UI, and data-dense layouts.
- High learning velocity across AI APIs, machine learning tooling, cloud foundations, and full-stack architecture.