I build practical AI systems for business data and automation.
I'm Shahid Ali, an AI engineer pursuing a Master's in Artificial Intelligence at Università di Verona. I build AI chatbots, RAG systems, data analysis agents, machine learning dashboards, computer vision models, and automation tools that help teams answer questions faster, predict risks, and make better decisions.
Professional Profile
AI Engineer
AI
Master’s Student
7+
AI/ML Projects
RAG
Chatbots & Agents
RL
Thesis Focus
Current Research
Reinforcement Learning for Building Energy Management using CityLearn, SAC, PPO, and Stable Baselines3.
Services
Chatbots, RAG, dashboards
Stack
OpenAI, LangGraph, ML
Focus
Business AI solutions
AI Services
What I can build for companies
I design practical AI systems that help businesses automate workflows, analyze data, predict risks, and make better decisions.
AI Chatbots & RAG Systems
Custom AI assistants, PDF Q&A systems, knowledge-base chatbots, and document search tools grounded in your business data.
AI Data Analyst Agents
Upload datasets, ask questions, generate insights, build charts, detect anomalies, and export reports.
ML Dashboards & Prediction Models
Production-minded dashboards for risk prediction, churn prediction, demand forecasting, anomaly detection, and operational analytics.
AI Automation Workflows
OpenAI, LangChain, and LangGraph workflows that automate research, reporting, analysis, and decision support.
01 / Discover
Clarify the business goal, users, data sources, risks, and success metrics.
02 / Prototype
Build a focused AI proof of concept with measurable outputs and clear limitations.
03 / Ship
Deploy a clean, documented system that is ready for feedback, iteration, and Vercel hosting.
Projects
Selected AI projects
Real AI applications covering business analytics, RAG systems, agentic workflows, machine learning dashboards, and computer vision.
AI Supply Chain Risk Management Dashboard
AI-powered Streamlit dashboard for predicting supply chain risk, monitoring inventory and demand, detecting anomalies, and generating AI-assisted management reports from CSV or Excel datasets.
AI Data Analyst Agent
Advanced AI data analyst agent that lets users upload CSV or Excel files, ask questions, generate insights, build charts, detect anomalies, clean data, train ML models, and export reports.
Autonomous Research Assistant
Agentic AI system that searches web sources, arXiv, and Wikipedia, summarizes findings, ranks source reliability, stores notes, and exports research briefs as Markdown and PDF.
Legal Document Q&A System
PDF question-answering app with Chroma vector store, citation-aware answers with file and page references, and embedding fallback support.
Research Navigator
Streamlit application for discovering and previewing arXiv research papers with NLP-powered search and summarization.
Facial Emotion Recognition
Computer vision project for detecting emotions such as happy, sad, and angry using FER2013 and CNN-based models.
Customer Churn Prediction
Machine learning project to predict which users are likely to leave a service using supervised learning models.
Blog
AI engineering notes
Short, practical articles planned around the same systems I build: RAG assistants, agent workflows, analytics, and applied AI research.
How I think about production RAG chatbots
A practical breakdown of retrieval quality, source-grounded answers, prompt design, and user experience for business assistants.
Preview article
Building AI data analyst agents for real workflows
Notes on turning CSV and Excel analysis into a useful agent experience with charts, anomaly checks, and exportable reports.
Preview article
Reinforcement learning for building energy management
Research notes from CityLearn, SAC, PPO, and multi-agent control for smarter building energy optimization.
Preview article
Skills
Technical toolkit
A focused toolkit for building AI-powered apps, dashboards, automation systems, and intelligent assistants.
Domains
Applications
Frameworks & Tools
Libraries & Languages
Experience
Research and engineering background
Academic research and hands-on AI engineering experience across machine learning, deep learning, and reinforcement learning.
Università di Verona
Research Intern - Reinforcement Learning
Apr 2026 - Present
- Building custom CityLearn environments for multi-agent reinforcement learning in building energy management.
- Analyzing and comparing two state-of-the-art MARL algorithms in terms of complexity and performance.
- Training RL agents using SAC and PPO with Stable Baselines3.
Università di Verona
Machine Learning Engineer Intern
Mar 2026 - May 2026
- Developed machine learning models for classification and regression tasks using Python and scikit-learn.
- Cleaned and preprocessed raw datasets using pandas and NumPy to improve model performance.
- Trained and validated models such as Linear Regression, Logistic Regression, Random Forest, and Support Vector Machines.
Università di Verona
Deep Learning Intern
Dec 2024 - Feb 2025
- Designed and implemented deep learning models for image classification and prediction.
- Built and trained convolutional neural networks for computer vision projects.
- Evaluated models using accuracy, precision, recall, and F1 score.
- Collaborated with team members to integrate trained models into application pipelines.
Education
Academic background
Università degli Studi di Verona
Master of Science in Artificial Intelligence
Verona, Italy
Oct 2023 - Present
Coursework in reinforcement learning, computer vision, explainable AI, deep learning, and data-intensive computing. Thesis on multi-agent RL for building energy management using CityLearn 2023.
Mehran University of Engineering and Technology
Bachelor of Engineering in Industrial Engineering & Management
Jamshoro, Pakistan
Dec 2018 - Dec 2022
Foundation in engineering principles, operations research, optimization, and industrial management systems.
Languages
Thesis / Research (In Progress)
Reinforcement Learning for Building Energy Management
CityLearn 2023 Control Track • Started Feb 2026
Developing a custom RL environment with real physical components including PV solar panels, battery, HVAC, and DHW storage for multi-building energy optimization.
Training multi-agent reinforcement learning policies using SAC and PPO via Stable Baselines3, comparing algorithm complexity and performance across different building configurations.
Contact
Let's build something intelligent
Reach out for AI chatbots, RAG systems, data analysis agents, machine learning dashboards, computer vision apps, or research collaboration.