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AI Engineer with less than a year in Deep Learning, NLP, and Computer Vision projects.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with a Bachelor of Science in Artificial Intelligence from FAST National University of Computer and Emerging Sciences. Skilled in Python, Deep Learning, Natural Language Processing, and Computer Vision. Experienced in developing AI-driven online assessment platforms, phishing detection models, urban surveillance systems, and MLOps CI/CD pipelines with Docker and AWS.
FAST National University of Computer and Emerging Sciences
Bachelor of Science · Artificial Intelligence
January 1, 2022 – June 1, 2026
ProctoGrade: AI-Powered Online Assessment & Proctoring Platform
June 1, 2025 – June 1, 2026
Built an AI-driven online examination system using Llama 3 and FAISS to generate rubric-based questions and support intelligent student practice through RAG. Implemented automated grading and exam monitoring pipelines, achieving 92% violation detection accuracy during system evaluation.
Phishing Email Detection: Zero-Shot vs Fine-Tuned NLP Models
June 1, 2024 – December 1, 2024
Developed phishing detection models using zero-shot and fine-tuned transformer approaches on labeled email datasets. Compared model performance using Accuracy, Precision, Recall, and F1-Score, analyzing trade-offs between speed and detection quality.
Smart City Urban Surveillance Computer Vision System
January 1, 2024 – May 1, 2024
Fine-tuned YOLOv8 for real-time pedestrian detection on urban datasets, achieving 83% Precision. Optimized inference using TensorRT to sustain 30+ FPS on low-power edge deployment hardware.
MLOps CI/CD Pipeline With Docker & AWS Deployment
September 1, 2023 – December 1, 2023
Designed an end-to-end MLOps pipeline with automated CI/CD using GitHub Actions and Dockerized ML inference APIs. Deployed scalable services on AWS EC2 with FastAPI, integrating S3 storage and Airflow-based workflow automation.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and foundational skill set in AI, aligning well with an AI Engineer role. The diversity of projects across NLP, Computer Vision, and MLOps indicates a broad technical curiosity. However, all experience is academic, which might require mentorship in a professional setting. The absence of extracurricular activities or team-based project descriptions makes it difficult to fully assess cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems. The academic nature of all projects suggests a strong learning aptitude and theoretical understanding. However, without professional experience or psychometric test results, it is difficult to assess operational fit, teamwork, or stress handling capabilities.