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Chee Nue Lor

I'm a Lead Full Stack Al Systems Engineer Architect of Scalable Intelligence

Result-driven Lead Full Stack & Al Engineer with 6+ years of hands-on experience architecting, deploying, and scaling intelligent SaaS, fintech, and automation platforms. Expert in Python, Node.js, React, and advanced Al/ML ecosystems (PyTorch, TensorFlow, LangChain). Proven ability to lead cross-functional teams, implement efficient CI/CD pipelines, and deliver high-availability cloud systems serving global clients. Recognized for translating complex Al research into scalable, secure, and cost-efficient production systems.

Download Resume/CV

My Services

Web App Development

I develop fast, scalable APIs and dashboards using FastAPI, Flask, Streamlit, React, and Tailwind.

Artificial Intelligence

I design and deploy intelligent systems including chatbots, LLM-powered summarizers, and smart assistants using GPT, BERT, and HuggingFace Transformers.

Machine Learning Solutions

I build ML models for classification, regression, and clustering — from data preprocessing to deployment with MLflow and Docker.

Deep Learning & NLP

Skilled in computer vision, NLP, and LLM fine-tuning using PyTorch, TensorFlow, and HuggingFace Transformers.

Data Science

I apply statistical analysis, machine learning, and visualization techniques to extract insights and deliver impactful solutions across healthcare, finance, and cybersecurity.

Data Analysis

Proficient in data wrangling, hypothesis testing, and dashboarding using Pandas, SQL, Excel, Power BI, and Tableau — helping businesses make data-driven decisions.

Data Visualization

I deliver interactive dashboards and charts using Tableau, Power BI, Seaborn, and Plotly.

Anomaly Detection & IoT Security

I build intrusion detection models using SVM, Autoencoders, GANs, and apply them to NSL-KDD & IoT networks.

Research & Technical Writing

I assist with academic papers, research methodology, and publication in Q1/Q2 journals for AI/ML topics.

Why Hire Me?

Result-driven Lead Full Stack & Al Engineer with 6+ years of hands-on experience architecting, deploying, and scaling intelligent SaaS, fintech, and automation platforms. Expert in Python, Node.js, React, and advanced Al/ML ecosystems (PyTorch, TensorFlow, LangChain). Proven ability to lead cross-functional teams, implement efficient CI/CD pipelines, and deliver high-availability cloud systems serving global clients. Recognized for translating complex Al research into scalable, secure, and cost-efficient production systems.

My Experience

A blend of freelance, internship, leadership, and content‑creation roles where I delivered impactful AI/ML solutions.

04/2023 – Present | Remote

Lead Full Stack & Al Engineer, Freelance / Contract

  • Delivered 20+ Al-driven SaaS and fintech systems integrating GPT-based workflows, automation, and predictive analytics.
  • Architected multi-agent automation pipelines (Python + Node.js) reducing manual operations by 85%.
  • Built real estate forecasting models (6-2 months) enabling clients to achieve 22% more accurate investment decisions.
  • •Deployed Dockerized CI/CD pipelines cutting deployment times from 30 min to 5 min.
  • Led team of 5 engineers, mentored juniors, and standardized system design reviews improving output by 25%.
  • Implemented enterprise-grade security protocols (OAuth2, JWT) ensuring compliance and data integrity.

06/2021 – 03/2023 | Remote

Senior Full Stack Engineer, Luminaa.io

  • Scaled multi-tenant SaaS infrastructure with NestJS + PostgreSQL, improving performance by 35%.
  • Developed an Al recommendation engine increasing engagement and retention by 12%.
  • Directed Kubernetes-based scaling achieving 99.99% uptime across microservices.
  • Collaborated with product and data teams to deliver customer-centric Al solutions serving 50K+ users.
  • Introduced monitoring via Prometheus + Grafana, enhancing observability and incident response by 0%.

06/2019 – 05/2021 | New York, NY

AI/ML Research Engineer, OpenAl Hackathon Circuit

  • Designed NLP and reinforcement learning models for automation and knowledge extraction.
  • Built interpretable Al dashboards (SHAP, LIME) ensuring transparency in ML models.
  • Fine-tuned transformer-based architectures achieving state-of-the-art results.
  • Published research insights and open-source tools used by 1K+ developers.

My Education

Academic journey enriched with data‑science, AI, and software‑engineering expertise.

2015 – 2019

B.S. Computer Science

New York University, NY

2024

Web Development Certificate

Programming Hero

  • HTML, CSS, JS, React, Tailwind

2025

Deep Learning Specialization

DeepLearning.AI (Coursera)

  • CNNs, RNNs, LSTMs with TensorFlow/Keras

2025

Statistics with Python

University of Michigan

  • Statistical inference & regression in Python

2025

SQL for Data Science

Coursera

  • Advanced querying, joins & analytics

2025

Python for Everybody

University of Michigan

  • APIs, databases & data structures

My Skills

Core technologies, frameworks, and tools I use daily.

React.js Node.js Express MongoDB Python TensorFlow PyTorch scikit‑learn Docker SQL AWS / Azure Git & GitHub Power BI / Tableau

About Me

Name: Chee Nue Lor
Gender: Male
Age: 28 Years
City: New York, NY
Nationality: United States
Full‑Time: Available
Freelance: Available
Phone: (+1) 2126399675
Email:fusiondev.ai117@gmail.com
Languages: English

Latest Projects

01

AIU Smart Resume Analyzer

Web app that automates resume screening with NLP, JWT security and an ATS-style scoring engine.

FastAPI · Firebase · spaCy · MLflow
More Details

Problem Statement

Traditional résumé reviews are slow and prone to bias.

Key Findings

  • 92% entity extraction accuracy.

Future Work

  • LLM feedback + OTP login.
Smart Resume Analyzer Preview
02

Real-Time Facial Liveness Detection (No DL)

CNN-free anti-spoofing: HOG · LBP · Gabor + SVM / RF / XGBoost → 100 % on iBeta.

OpenCV · scikit-learn · XGBoost
More Details

Problem Statement

Deep models are heavy for mobile/IoT; need lightweight yet robust liveness detection.

Key Findings

  • SVM (RBF) hits perfect accuracy with ≈ 8 ms inference on Raspberry Pi 4.

Future Work

  • Add blink / head-motion temporal cues.
Facial Liveness preview
03

AI-Powered Research Paper Summarizer

Transformer models (Pegasus / T5) auto-extract & summarise Introduction → Conclusion.

Python · Transformers · ROUGE
More Details

Problem Statement

Researchers struggle to scan hundreds of papers quickly.

Key Findings

  • Summaries score ROUGE-L 0.44 vs. human abstracts.

Future Work

  • Interactive Q&A chat over PDFs (LLMs).
Paper summarizer preview
04

Predicting Falcon 9 Landing Success

Decision-Tree hits 96 % using SpaceX public API data.

Python · Plotly · Dash · scikit-learn
More Details

Problem Statement

Optimise booster reuse costs.

Key Findings

  • Payload mass negatively correlated with landing success.

Future Work

  • Integrate weather & wind features.
Falcon 9 dashboard
05

Crime Rates by Region — Visual Analysis

K-Means clusters 50 US states by violent-crime patterns (1973 data).

Python · Seaborn · Folium
More Details

Problem Statement

Lack of intuitive crime dashboards for policy makers.

Key Findings

  • Urban population is the strongest correlate.

Future Work

  • Add 50-year time-series & socioeconomic factors.
Crime heatmap
06

Statistical Income Classification (R)

Naïve Bayes model classifies Adult Census income with R visualisations.

R · ggplot2 · dplyr
More Details

Problem Statement

Understand demographic drivers of income.

Key Findings

  • Age & hours/week are key predictors.

Future Work

  • Switch to Gradient Boosting.
Income plots
07

African Credit Scoring — Deep Learning

Tabular DNN with polynomial features & threshold tuning for F1.

TensorFlow · Keras
More Details

Problem Statement

Reduce default risk for micro-finance lenders.

Key Findings

  • DNN outperforms LightGBM in recall.

Future Work

  • SHAP explanations & Streamlit app.
Credit scoring dashboard
08

Customer Churn Prediction (ML)

LightGBM + SHAP explainability on 10 000 banking customers.

LightGBM · SHAP · pandas
More Details

Problem Statement

Proactive retention of high-value clients.

Key Findings

  • Age & balance are top predictors.

Future Work

  • Deploy REST API on FastAPI.
Churn SHAP plot
09

Predicting Student Performance (ML)

Random Forest hits 91 % accuracy on behavioural & demographic data.

Python · scikit-learn · pandas
More Details

Problem Statement

Early-alert system for at-risk students.

Key Findings

  • Absenteeism (corr –0.61) is the strongest negative factor.

Future Work

  • Real-time dashboard for teachers.
Student performance chart
10

Hybrid Anomaly Detection in IoT Networks

Compares SVM vs Autoencoder, CNN, LSTM & GAN on NSL-KDD.

TensorFlow · NSL-KDD · scikit-learn
More Details

Problem Statement

Secure resource-constrained IoT devices from cyber-attacks.

Key Findings

  • LSTM achieved ROC-AUC 0.98.

Future Work

  • Edge-friendly federated learning.
IoT anomaly plot

Let's Work Together

Phone

(+1) 2126399675

Email

fusiondev.ai117@gmail.com

Contact

Telegram : devmatt000

Discord : devmatt000

Contact Me!