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AI Engineer with less than a year in AI with hands-on experience in Machine Learning, Data Analysis,
Highly motivated Computer Science student specializing in Artificial Intelligence with hands-on experience in Machine Learning, Data Analysis, and Large Language Model (LLM) applications. Skilled in developing end-to-end AI solutions, data-driven applications, and intelligent automation systems using Python and modern AI frameworks. Experienced in data preprocessing, model training, visualization, and local AI deployment. Passionate about solving real-world problems through Artificial Intelligence, Machine Learning, and Agentic AI systems while continuously exploring emerging technologies and industry best practices.
University of technology Nowshera
Bachelor of Science · Computer Science, Artificial Intelligence
August 1, 2024 – June 30, 2028
Local AI Chatbot System using Ollama & Phi Model
June 1, 2025 – June 1, 2026
Architected and deployed a fully functional AI chatbot running locally on CPU, eliminating cloud dependencies and ensuring complete data privacy. Integrated Ollama runtime with Phi language model, implementing intelligent prompt handling and context management for coherent conversations. Designed modular system architecture demonstrating deep understanding of LLM inference, tokenization, and model serving patterns. Gained practical experience in prompt engineering, model configuration, and offline AI deployment strategies.
End-to-End Data Analysis & Visualization Project
June 1, 2025 – June 1, 2026
Executed complete data pipeline: importing, cleaning, preprocessing, and transforming raw datasets using Pandas and NumPy. Created compelling visual insights using Matplotlib and Seaborn, generating publication-ready charts and graphs. Performed exploratory data analysis (EDA) to uncover patterns, correlations, and actionable insights from structured datasets.
Machine Learning Classification & Regression Models
June 1, 2025 – June 1, 2026
Implemented multiple supervised learning models: Linear Regression, Logistic Regression, and multi-class classification algorithms. Mastered complete ML workflow: data preprocessing, train-test splitting, model training, hyperparameter tuning, and comprehensive evaluation. Applied cross-validation and performance metrics (accuracy, precision, recall, F1-score, confusion matrices) for robust model assessment.
Cultural Fit Analysis
The candidate's academic projects demonstrate initiative and a proactive approach to learning and applying AI concepts. The focus on local AI deployment and end-to-end data pipelines shows a practical, problem-solving mindset. The stated interest in emerging technologies like Agentic AI suggests a curiosity and drive for continuous learning, which can be a good cultural fit for innovative teams. However, the lack of professional experience or diverse project types limits the assessment of broader cultural adaptability.
Soft Skills & Operational Fit
The candidate's resume indicates a highly motivated individual passionate about solving real-world problems with AI. The project descriptions suggest an ability to work independently on technical challenges. However, without psychometric or direct interview data, it's difficult to assess stress handling, team collaboration, or specific work attitudes.