Result-driven AI/ML Engineer with 6+ years of experience building, training, and deploying intelligent systems at scale. Expert in deep learning, natural language processing, and predictive modeling, with a proven ability to take models from prototype to production. Strong background in MLOps, cloud deployment, and full-stack integration, combining data science rigor with software engineering excellence.
I apply statistical analysis, machine learning, and visualization techniques to extract insights and deliver impactful solutions across healthcare, finance, and cybersecurity.
Proficient in data wrangling, hypothesis testing, and dashboarding using Pandas, SQL, Excel, Power BI, and Tableau — helping businesses make data-driven decisions.
I design and deploy intelligent systems including chatbots, LLM-powered summarizers, and smart assistants using GPT, BERT, and HuggingFace Transformers.
I build ML models for classification, regression, and clustering — from data preprocessing to deployment with MLflow and Docker.
Skilled in computer vision, NLP, and LLM fine-tuning using PyTorch, TensorFlow, and HuggingFace Transformers.
I develop fast, scalable APIs and dashboards using FastAPI, Flask, Streamlit, React, and Tailwind.
I deliver interactive dashboards and charts using Tableau, Power BI, Seaborn, and Plotly.
I build intrusion detection models using SVM, Autoencoders, GANs, and apply them to NSL-KDD & IoT networks.
I’m a results‑driven Machine Learning Engineer & Data Scientist who turns raw data into deployable, production‑grade solutions. From fine‑tuning LLMs and building MLOps pipelines with MLflow & Docker, to creating interactive dashboards in Power BI, I deliver end‑to‑end value across healthcare, finance, and IoT security.
A blend of freelance, internship, leadership, and content‑creation roles where I delivered impactful AI/ML solutions.
04/2023 – Present | Remote
06/2021 – 03/2023 | Remote
06/2019 – 05/2021 | New York, NY
Academic journey enriched with data‑science, AI, and software‑engineering expertise.
2015 – 2019
New York University, NY
2024
Programming Hero
2025
DeepLearning.AI (Coursera)
2025
University of Michigan
2025
Coursera
2025
University of Michigan
Core technologies, frameworks, and tools I use daily.
01
Web app that automates resume screening with NLP, JWT security and an ATS-style scoring engine.
Traditional résumé reviews are slow and prone to bias.
CNN-free anti-spoofing: HOG · LBP · Gabor + SVM / RF / XGBoost → 100 % on iBeta.
Deep models are heavy for mobile/IoT; need lightweight yet robust liveness detection.
Transformer models (Pegasus / T5) auto-extract & summarise Introduction → Conclusion.
Researchers struggle to scan hundreds of papers quickly.
Decision-Tree hits 96 % using SpaceX public API data.
Optimise booster reuse costs.
K-Means clusters 50 US states by violent-crime patterns (1973 data).
Lack of intuitive crime dashboards for policy makers.
Naïve Bayes model classifies Adult Census income with R visualisations.
Understand demographic drivers of income.
Tabular DNN with polynomial features & threshold tuning for F1.
Reduce default risk for micro-finance lenders.
LightGBM + SHAP explainability on 10 000 banking customers.
Proactive retention of high-value clients.
Random Forest hits 91 % accuracy on behavioural & demographic data.
Early-alert system for at-risk students.
Compares SVM vs Autoencoder, CNN, LSTM & GAN on NSL-KDD.
Secure resource-constrained IoT devices from cyber-attacks.
Phone
(+1) 2126399675
chee.devmatt.tech@gmail.com
Contact
Telegram : devmatt000
Discord : devmatt000