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AI Engineer with 1+ years in AI/ML and full-stack web development.
Final-year BS Artificial Intelligence student at COMSATS University Attock (CGPA: 3.59) with hands-on experience building production-grade AI applications. Developed FATOR a full-stack AI debate coaching platform integrating Groq's LLAMA 3.3 70B via API, OpenAI Whisper for multilingual speech-to-text, and a custom-trained LinearSVC fallback model on 29,412 debate samples. Experienced in API integration, hybrid ML architectures, deep learning (CNNs, YOLO), and full-stack web development. Proficient in Python, JavaScript, PHP, TensorFlow, Keras, and Supabase. Aspiring to contribute to AI engineering, web development and NLP-driven product development.
COMSATS University Attock
Bachelor of Science · Artificial Intelligence
August 1, 2022 – June 30, 2026
Code Alpha
Machine Learning Intern
August 1, 2024 – August 31, 2024
India
Online Publication Bookstore (OBR)
June 1, 2025 – July 31, 2025
Developed a full-stack bookstore web application using PHP, MySQL, HTML, and CSS. Features include user browsing interface, admin dashboard, secure session-based login, and full CRUD operations.
Stationary Object Detection – YOLO + Roboflow
May 1, 2025 – June 30, 2025
Trained a custom YOLO-based object detection model using Roboflow for real-time detection via webcam feed. Managed dataset annotation, model training pipeline, performance tuning, and live deployment.
FATOR – AI Debate Coaching Platform (Final Year Project)
April 1, 2025 – June 30, 2026
Built a production-grade web platform that analyzes debate speeches in real time – combining content analysis (what was said) with audio metrics (how it was said). Integrated Groq API (LLAMA 3.3 70B) for intelligent argument critique, logical fallacy detection, and structured feedback generation in English and Urdu. Implemented OpenAI Whisper (large-v2) for accurate multilingual speech-to-text transcription supporting English and Urdu input. Trained a custom LinearSVC classifier on 29,412 debate samples (Feedback Prize dataset) achieving 58.7% accuracy as an intelligent fallback when Groq API is unavailable – ensuring near 100% system uptime. Built real-time audio analysis pipeline using Librosa (pitch, tempo, energy, pause ratio) with interactive Plotly visualizations and WaveSurfer.js waveform display. Designed role-based access control (student / coach / admin) using Supabase Auth and PostgreSQL; stored audio files and PDF reports in Supabase Storage. Generated downloadable PDF session reports using jsPDF and html2canvas with custom Urdu RTL text rendering via canvas-based Noto Nastaliq font. Implemented Service Worker for offline functionality and graceful degradation across all API failure scenarios. Frontend built with HTML5, CSS3, and ES6+ JavaScript; backend in Python with direct Groq and Whisper API calls (no LangChain – custom prompting pipeline).
Credit Scoring Model
August 1, 2024 – September 30, 2024
Developed a binary classification model using Random Forest to predict loan default risk from financial data. Applied data preprocessing, feature scaling, and SMOTE to handle class imbalance; achieved strong evaluation metrics.
Build with AI Workshop
Google Developer Groups (GDG)
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a diverse interest in AI applications, from debate coaching to object detection and credit scoring, indicating a broad curiosity and willingness to tackle different problem domains. The final year project, FATOR, shows a strong initiative and ability to work on a complex, multi-faceted system, which aligns well with an innovative and project-driven culture. The academic background in AI further strengthens the fit for an AI Engineer role. However, the experience is primarily academic and internship-based, which might require some adjustment to a fast-paced industry environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and a drive to build functional, robust systems, including handling API failures and offline functionality. The academic background and project diversity suggest a good learning curve and adaptability. However, without specific psychometric or behavioral assessment data, it's difficult to fully assess work attitude, stress handling, or team collaboration beyond what can be inferred from project scope.