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AI Engineer with 1+ years in multi-agent systems, RAG, and MLOps.
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Assessing your cultural and operational fit
AI Engineer with 1.3 years of experience specializing in the development and deployment of AI-driven solutions. Proven expertise in building multi-agent systems, implementing Graph RAG pipelines, and automating business processes with AI. Skilled in LangGraph, LangChain, Neo4j, Docker, and AWS, with a strong focus on improving efficiency and accuracy in AI applications.
Cairo University
B.Sc. · Computers Science and Artificial Intelligence
August 1, 2022 – June 30, 2026
Datalentech
AI Intern
March 1, 2026 – May 31, 2026
India
Shadi Systems
AI Systems Intern
March 1, 2026 – May 31, 2026
India
Echo Mira
AI Automation Intern
December 1, 2025 – February 28, 2026
India
Instant Software Solutions
AI Mentor
August 1, 2025 – February 28, 2026
India
ZeeVice.ai
June 27, 2026 – Present
Built a full-stack AI e-commerce platform with a Graph RAG pipeline (Neo4j + ChromaDB) that reduced irrelevant product recommendations by 45% compared to baseline vector-only search, by scraping 10,000+ products from B.Tech, 2B, Raya, and Noon via BeautifulSoup & Selenium, and integrating LLaMA 4 function calling for conversational product recommendations across a 50k-node knowledge graph.
View ProjectResearch Generator
June 27, 2026 – Present
Built an end-to-end AI research automation platform using LangGraph multi-agent orchestration and RAG with Tavily search that generated fully cited 10,000-word papers in under 4 minutes, featuring multi-LLM orchestration, an async Flask backend with real-time status streaming, and a source verification system across a React TypeScript frontend.
View ProjectAchieved 88% on the 'Data Scientist — Artificial Intelligence' exam, indicating a strong grasp of the subject matter and high proficiency in relevant skills.
Strengths
Limitations
Cultural Fit Analysis
The candidate's diverse project portfolio, including personal projects like 'Research Generator' and 'ZeeVice.ai', alongside multiple internships, indicates a strong passion for AI and a proactive learning attitude. Their experience mentoring others also suggests a collaborative and knowledge-sharing mindset. The breadth of technologies and problem domains tackled (e-commerce, research automation, CV scoring, customer service automation) shows adaptability and a willingness to explore different applications of AI, which aligns well with an innovative and dynamic team culture.
Soft Skills & Operational Fit
The candidate's experience as an AI Mentor suggests strong communication and pedagogical skills. Project descriptions indicate an ability to work on complex, multi-faceted problems, implying good problem-solving and potentially teamwork skills. The quantifiable results in their projects (e.g., reducing screening time by 60%, improving customer satisfaction by 65%) demonstrate a results-oriented approach. The psychometric test score of 340/500 suggests moderate performance in areas like logical reasoning, work attitude, stress handling, and team collaboration, which could be an area for further exploration.