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AI Engineer with less than a year in MLOps and Generative AI
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 graduate (NUML, Islamabad) with production ML/MLOps experience shipping pipelines on 80K+ records at 88% accuracy. Proficient in Python, FastAPI, Docker, MLflow, DVC, LangChain, and XGBoost. Hackathon winner (Vyrothon 2026, 1st/500+) with end-to-end delivery across RAG, MLOps, and computer vision systems. Ready to contribute immediately to a fast-moving engineering team.
The candidate achieved a perfect score (100%) on the 'Data Scientist — Artificial Intelligence' exam, indicating comprehensive knowledge and proficiency in the evaluated skills.
Strengths
Cultural Fit Analysis
The candidate's project diversity (virtual try-on, purchase intent, churn prediction, RAG engine) and hackathon achievement suggest an innovative and driven individual. The experience as an AI/ML Intern at SiberKoza Alpha indicates exposure to a professional engineering environment. The target role of AI Engineer aligns well with the candidate's technical skills and project experience, particularly in MLOps and various AI domains. The certifications in Oracle Cloud Infrastructure AI also show a commitment to cloud-based AI solutions.
Soft Skills & Operational Fit
The psychometric test score is below average, suggesting potential areas for development in logical reasoning, work attitude, stress handling, or team collaboration. However, the candidate's hackathon win and project descriptions imply a proactive and results-oriented approach. Further assessment during interviews would be beneficial to understand the nuances of their operational fit and soft skills.
Limitations