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AI Engineer with 5+ years in Generative AI & Deep Learning
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Assessing your cultural and operational fit
AI Engineer with 5.6 years of experience in developing Generative AI solutions and deep learning models. Proficient in designing RAG-based chatbots, building multi-source knowledge assistants, and creating object detection and classification systems. Proven ability to enhance contextual understanding in chatbots and achieve high accuracy in image and time-series tasks, leveraging Python, LangChain, Pinecone, OpenAI, and various deep learning frameworks.
National College of Business Administration & Economics, Lahore
Bachelor of Science · Computer Science
N/A – June 30, 2026
Techveus
ML Engineer
February 1, 2025 – August 1, 2025
Lahore, Punjab, Pakistan
National Center of Artificial Intelligence (NCAI)
AI Engineering Research Internship
January 1, 2024 – October 1, 2024
Lahore, Punjab, Pakistan
Al-Mehraj Academy, Al-Hamd Science Academy & Private Tutoring
STEM Tutor (Home-Based & Academic Institutes)
April 1, 2022 – Present
Lahore, Punjab, Pakistan
Fresh vs. Rotten Fruit Detection (YOLOv5 & YOLOv8)
June 1, 2026 – Present
Built a real-time object detection system to classify fresh vs. rotten fruits using custom-annotated datasets. Trained YOLOv5 achieving 92% mAP, and YOLOv8 achieving 95% mAP for better precision-recall balance. Dataset: Kaggle - Fresh and Rotten Fruits Dataset.
Document Chatbot (LangChain, Pinecone, OpenAI)
June 1, 2026 – Present
Developed a context-aware Q&A chatbot capable of handling large PDFs and text documents. Integrated embeddings + retrieval pipelines to generate accurate and source-grounded responses. Reduced irrelevant answers by 40% compared to baseline OpenAI Q&A. Dataset: Used public PDF documents (e.g., research papers, Wikipedia dumps).
Multi-Source Knowledge Assistant (LangGraph, Wikipedia API, LangChain, OpenAI)
June 1, 2026 – Present
Designed a LangGraph workflow that dynamically queries multiple knowledge sources (Wikipedia API + PDFs + user-uploaded docs). Ensured context-driven responses with fallback strategies, improving accuracy in multi-turn conversations. Showcased knowledge routing between structured/unstructured sources.
Skin Cancer Detection (ResNet50 + Flask Deployment)
June 1, 2026 – Present
Built a deep learning classifier to detect malignant vs. benign skin cancer using ResNet50 transfer learning. Achieved 93% accuracy with augmented dermoscopic images. Deployed as a Flask web app with Pandas/Matplotlib for reporting. Dataset: ISIC Skin Cancer Dataset.
Brain Tumor Classification (CNN + Transfer Learning, Streamlit Deployment)
June 1, 2026 – Present
Developed a CNN-based classifier with transfer learning to detect Glioma, Meningioma, Pituitary tumors, and No Tumor from MRI scans. Achieved 96% accuracy after tuning hyperparameters and applying data augmentation. Deployed via Streamlit for interactive usage by medical researchers. Dataset: Brain Tumor MRI Dataset.
Research Intern at National Centre of Artificial Intelligence (NCAI)
National Centre of Artificial Intelligence (NCAI)
June 1, 2026 – Present
NAVTTC AI(ML,DL) Course with A+ grade
Computer Science Department, Uet Lahore
June 1, 2026 – Present
Teaching Experience | Delivered private and institutional tutoring in Mathematics, Physics, Computer.
Unknown
April 1, 2022 – Present
Cultural Fit Analysis
The candidate's project portfolio is diverse, covering computer vision, NLP, and generative AI, which aligns well with the broad scope often found in AI engineering roles. The experience at NCAI and Techveus, along with personal projects, shows initiative and a continuous learning mindset. The current part-time tutoring role indicates a commitment to education and potentially strong interpersonal skills. The breadth of skills and project types suggests adaptability and a willingness to tackle different challenges, contributing positively to cultural fit.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving approach and an ability to work with diverse datasets. The experience as a STEM tutor suggests good communication and teaching skills, which can be beneficial for team collaboration and knowledge sharing. However, without specific psychometric or English test results, a comprehensive assessment of operational fit and soft skills is limited.