AI / ML / Data Analytics / Full-Stack AI resume
Sohail H.
Applied AI/ML and full-stack AI application professional with work spanning data analytics, predictive modeling, deep learning, computer vision, generative AI, RAG, and a live production SaaS application.
Portfolio proof
7AI/ML case studies
1Live production AI SaaS
7Public AI/ML repositories
AI + Full-StackApplied delivery
Core capabilities
Applied AI, ML, analytics, and full-stack AI
Python · Pandas · NumPy · Scikit-learn · TensorFlow · Keras · EDA · classification · regression · ensemble learning · neural networks · CNNs · transfer learning · RAG · embeddings · semantic search · prompt engineering · Next.js · Node.js · TypeScript · JavaScript · OpenAI API · Supabase · Stripe · Vercel · API integration · Flask · Streamlit · Docker · GitHub.
Selected projects
One production application + seven completed AI/ML case studies
ListingsEdge AI — Built and launched a production full-stack AI SaaS for real-estate marketing using Next.js/Node.js, TypeScript, OpenAI API, Supabase, Stripe, and Vercel. The application generates MLS-ready descriptions, social captions, email content, property highlights, open-house content, and downloadable marketing materials. Visit live website
Medical Assistant — Built a retrieval-augmented assistant that grounds LLM responses in trusted medical reference materials using document processing, embeddings, semantic search, vector storage, and controlled prompting. View case study
SuperKart — Developed an end-to-end retail sales forecasting workflow spanning feature preparation, regression modeling, tuning, API packaging, containerization, and interactive delivery. View case study
HelmNet — Created a safety-helmet image classifier using CNNs, transfer learning, fine-tuning, and data augmentation to support automated workplace compliance monitoring. View case study
ReneWind — Designed neural-network classifiers for wind-turbine generator failure prediction with class-imbalance handling and recall-focused evaluation for proactive maintenance. View case study
EasyVisa — Built an ensemble-learning solution to predict visa application outcomes using bagging, boosting, stacking, hyperparameter tuning, and feature-importance analysis. View case study
Personal Loan Campaign — Developed a customer-propensity model for personal-loan targeting using exploratory analysis, feature preparation, classification models, and business-oriented evaluation. View case study
FoodHub — Performed end-to-end exploratory analysis of food-delivery orders to uncover customer behavior, restaurant performance, cuisine demand, ratings, delivery patterns, and revenue opportunities. View case study
Education
UT Austin Post Graduate Program
Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications.