Muhammad Iftikhar
I am AI Engineer in training
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Muhammad Iftikhar
AI Engineer in training
Motivated and detail-oriented AI Engineer in training, seeking an opportunity in applied machine learning, computer vision, or NLP projects. Passionate about solving real-world problems using deep learning, large language models (LLMs), and data-driven approaches. Eager to contribute to impactful AI products through hands-on development, research, and deployment.
Work Experience
Empowering Creativity through
2025
Dione Software
AI Intern
Worked on dataset labeling and annotation for object detection models. Trained YOLO models to detect vehicles (cars, trucks, bikes) in traffic videos. Contributed to training data pipelines and validation accuracy analysis.
My Skills
Core competencies that drive my performance.
Programming
Python90 %
C++90 %
JavaScript92 %
Bash94 %
SQL94 %
ML/AI Frameworks
Scikit-learn99 %
TensorFlow92 %
Keras97 %
PyTorch96 %
OpenCV94 %
YOLOv591 %
NLP
Hugging Face Transformers90 %
spaCy90 %
NLTK97 %
BERT90 %
GPT94 %
Tools
Git90 %
Kaggle92 %
Jupyter93 %
VS Code99 %
Linux96 %
Figma96 %
Flutter98 %
Languages
EnglishFluent %
UrduNative %
Education
Empowering Creativity through
2022 – 2026 (Expected)
BS in Computer Science (Specialization in AI)
Barani Institute of Information and Technology (BIIT)
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My Projects
Customer Experiences: A Tapestry of Happy Moments
AI-driven Call Audit System (Final Year Project)
Flutter-based admin app for automated auditing of customer calls using AI. Built an AI system to analyze call transcripts using NLP techniques. Integrated sentiment analysis, keyword spotting, and audit scoring modules. Designed Flutter frontend and used AI APIs for real-time feedback and audits.
Parameter-Efficient Supervised Fine-Tuning of LLaMA 3.2 (3B)
Medical chain-of-thought reasoning using LoRA-based fine-tuning on a PEFT setup. Used Unsloth to fine-tune LLaMA 3.2B on a medical reasoning dataset (Hugging Face). Implemented LoRA adapters for efficiency and tracked metrics using Weights & Biases. Developed a pipeline for preprocessing, training, and evaluation using PEFT.
Spam and Phishing Detector using NLP
Text classification model for detecting spam emails and phishing content. Cleaned and processed labeled datasets for email threat detection. Trained Logistic Regression and fine-tuned BERT for classification. Achieved over 95% precision on phishing content detection.
AI-Powered Personal Study Assistant (Flutter)
Semester project integrating AI for summarization, quizzes, and chat support. Implemented AI chatbot, summarizer, and MCQ generator using free APIs. Built an intuitive UI using Flutter and mockup tools like Figma and Visily.
House Price Prediction (Kaggle)
Regression-based ML model using Scikit-learn and XGBoost. Engineered features and cleaned data for predictive modeling. Tuned model hyperparameters with grid search and cross-validation.
Contact Information
Email : [email protected]
Phone : +92 305 4992947
LinkedIn : linkedin.com/in/zyrog
Github : github.com/zyrogX
Location : Islamabad, Pakistan
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