AI Automation, Data Scientist, Data Engineer, Educator
An end-to-end system for transforming written articles into polished social media video content This project demonstrates full-stack development and AI integration skills by automating the entire video production workflow—from script generation to social media publishing. Published Videos: Overview Key Features / Workflow Architecture AI Integration & APIs Video Processing & Automation Data Management Key Skills: …
Overview This project builds a full-stack automation workflow in n8n that streamlines blog content creation and publishing for WordPress. The system integrates multiple AI and API tools to enhance efficiency and maintain human feedback in the creative loop. List of published blog articles: Key Features / Workflow Architecture Outcome Website activity doubled within two months through consistent, …
A fully automated job discovery and application management system built using n8n, Apify APIs, Google Sheets, and LLM-based evaluation workflows. LinkedIn job Automation Workflow Upwork job scraper and analyzer
Overview An automated n8n workflow that intelligently scrapes, analyzes, and scores rental listings from 51.ca for the Toronto area, helping identify high-quality housing opportunities based on personalized criteria. Key Features Technical Stack Workflow Architecture Technical Highlights Skills Demonstrated Workflow Automation, Javascript, Web Scraping, LLM Integration, Data Engineering, API Integration, Chinese Language Processing, Google Sheets API, …
An integrated automation suite built with n8n that streamlines marketing intelligence workflows through API-driven data gathering, processing, and analysis. The system connects SEO, social, and web search data sources with real-time analytics pipelines to power content and keyword strategy SEO Keyword Research Automation Developed a dynamic SEO research workflow leveraging DataForSEO API to extract and analyze keyword data by …
Purpose:Set up an automated, scalable ML workflow using modern MLOps tools. Technologies Used: Key Tasks: Skills Applied: Result:A working ML deployment on Kubernetes and AWS, built for efficiency and repeatability. Link: Deploying Machine Learning Models with Kubeflow and KServe: A Comprehensive Guide | by Shijun Ju | Jun, 2025 | Medium Code: shj37/Minikube-Kubeflow-ML-KServe-AWS-Project All credit …
This project implements an automated MLOps pipeline for deploying a machine learning model, focusing on operational efficiency and security. It integrates Jenkins for CI/CD, Docker for containerization, and AWS ECS for scalable deployment, ensuring a repeatable and reliable workflow. Project Overview The pipeline automates the deployment of a Flask-based ML application, emphasizing Continuous Integration and …
This project demonstrates the deployment of a machine learning model using Docker and Kubernetes on Google Cloud Platform (GCP), highlighting skills in MLOps, containerization, and cloud infrastructure management. The process integrates a pre-trained PyCaret model into a Flask application, containerized with Docker, and orchestrated with Kubernetes for scalability and reliability. Project Overview Key Steps Outcomes …
This project is a hands-on implementation of a real-time algorithmic trading system that processes market data and news sentiment to make automated trading decisions. It integrates Apache Flink for stream processing, Redpanda for data streaming, and the Alpaca API for market data and trade execution, with sentiment analysis driving the strategy. Technologies Used How It …
This project is a real-time anomaly detection system for stock trading data, built with Redpanda and Quix. It processes streaming trade data and flags unusual patterns using both rule-based and machine learning techniques. Purpose The goal is to detect anomalies—like sudden price jumps or high-volume trades—as they happen, using a lightweight, practical setup. Technologies Used …