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AI Engineer with less than a year in NLP, Computer Vision & MLOps
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Junior AI/ML Engineer and Computer Engineering graduate with hands-on experience building end-to-end NLP, computer vision, and agentic AI pipelines. Skilled in PyTorch, transformer-based LLMs/LMMs, RAG pipelines, and MLOps workflows. Proven ability to fine-tune, quantize, and deploy models on AWS and GCP with measurable accuracy gains. Eager to contribute to data science and AI engineering teams as an entry-level professional.
Microsoft ML Track
Digital Egypt Pioneers Initiative (DEPI)
January 1, 2024 – Present
Shoubra Faculty of Engineering
B.Eng. · Computer Engineering
September 1, 2020 – May 1, 2025
Unknown
AI Engineer Intern
February 1, 2026 – Present
Cairo, Cairo Governorate, Egypt
Multi-modal Agentic AI System
January 1, 2024 – December 31, 2024
• Orchestrated a multi-agent system with specialized agents for text, image, and PDF document analysis using transformer-based LMMs and RAG retrieval. • Unified intent routing across audio, text, and visual modalities; monitored pipeline execution with AgentOps, reducing generation errors by 25%.
Car License Plate Detection System & Document Analysis
January 1, 2024 – December 31, 2024
• Built real-time vehicle and license plate detection pipeline achieving 98% detection accuracy and processing 25+ FPS on edge hardware, leveraging PyTorch for model training and evaluation. • Implemented data augmentation, noise reduction, and model quantization to produce a lean inference engine deployed on AWS, cutting inference time by 40% vs. baseline. • Tracked model performance with MLflow; used precision/recall/mAP metrics to guide iterative fine-tuning across 10+ experiment runs.
Prayer Posture Detection Model
January 1, 2023 – December 31, 2023
• Curated and annotated a custom dataset; trained a YOLO-based computer vision model reaching 97% precision and 30 FPS real-time edge deployment. • Applied model pruning and quantization techniques to optimize for edge hardware without sacrificing accuracy.
NLP with Attention Models
Unknown
June 1, 2026 – Present
Azure AI Fundamentals
Unknown
June 1, 2026 – Present
Transformer Architectures
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects showcase a strong interest in practical AI applications, from license plate detection to multi-modal agents. The diversity of projects (computer vision, NLP, agentic AI) indicates a broad curiosity and willingness to tackle different challenges, which aligns well with an innovative and dynamic team culture. Their ongoing education and certifications also suggest a commitment to continuous learning. However, the projects are primarily personal, and the internship is recent, so the extent of experience in collaborative, production-level environments is limited.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project descriptions, focusing on measurable outcomes like accuracy improvements and latency reduction. Their ability to work with various modalities and optimize for performance suggests a detail-oriented and results-driven approach. The internship experience indicates an ability to integrate into a professional team setting, though specific collaboration examples are not provided.