10+ years of enterprise IT experience meets cutting-edge AI. I build intelligent automation, consult for growing businesses, and solve the problems that keep your team up at night.
Currently open to full-time remote roles in AI/ML support and IT operations.
I'm a Machine Learning Engineer and IT Operations expert based in London, Ontario. With over a decade of hands-on experience in enterprise banking and MSP environments, I've built, automated, and scaled critical infrastructure for thousands of users.
Currently pursuing my AI & Machine Learning Graduate Certificate at Humber Polytechnic, I'm bridging my deep operational expertise with modern AI capabilities — building smarter tools, not just bigger teams.
Whether you need an AI consultant, a fractional IT lead, or a freelance ML engineer, I bring real enterprise discipline to every project.
Let's TalkBuilt a full end-to-end text classification system on 120,000+ real news articles (AG News dataset). Trained two models: a TF-IDF + Logistic Regression baseline and a Deep Neural Network using TensorFlow/Keras — comparing both on accuracy, precision, recall, and F1-score across 4 categories: World, Sports, Business, and Science/Technology.
🐙 View on GitHub →Analyzed 3,044 real startup funding records to answer 6 business questions. Built a full data cleaning pipeline handling Indian-style currency formatting, inconsistent city names, and messy funding types. Uncovered that Bangalore attracted ~$18B in funding, the median startup raises only $1.7M, and Sequoia Capital leads all investors. Delivered findings through dual-chart visualizations per question.
🐙 View on GitHub →Built and deployed a complete production-style ML pipeline on AWS. Loaded 11,162 bank customer records from S3, preprocessed with one-hot encoding (17 → 52 features), trained XGBoost on AWS compute, ran Bayesian hyperparameter tuning across 6 automated jobs, deployed a live real-time inference endpoint, and achieved 73.25% accuracy on 1,675 test samples. Endpoint properly deleted post-inference to manage cloud costs.
🐙 View on GitHub →Built two competing models to predict user access risk scores from cybersecurity signals (failed logins, access time deviation, location anomalies). DNN architecture (128→64→32→1) outperformed Random Forest on all metrics: MAE 4.43 vs 5.66, MAPE 11.6% vs 13.9%. Critically noted that despite lower accuracy, Random Forest may be preferable in production security systems for audit explainability — a trade-off that matters in regulated environments.
🐙 View on GitHub →Analyzed the 2019 World Happiness Report across 156 countries to identify what drives national happiness. Built a Multiple Linear Regression model using 6 socioeconomic indicators. Achieved R²=0.60, explaining 60% of happiness score variance. Key finding: GDP, Social Support, and Healthy Life Expectancy are the strongest predictors (correlation >0.78), while Generosity showed the weakest link (0.08).
🐙 View on GitHub →💼 Actively exploring full-time remote opportunities alongside consulting — open to the right role.
I help businesses automate repetitive workflows, integrate AI tools, and reduce operational costs using Python, Azure AI, and intelligent automation pipelines.
Don't need a full-time IT director? Get senior-level IT strategy, vendor management, and team leadership on a part-time basis — enterprise quality without the enterprise price tag.
Is your team working remotely? I'll audit your infrastructure, harden your security posture, set up VPNs, configure Azure AD, and ensure you're protected — one-time project.
Whether you're a hiring manager looking for a senior ML engineer, a startup that needs an AI consultant, or a business ready to automate — I want to hear from you.