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Mohd Fazil

Alīgarh, Uttar Pradesh, India

Since February 25, 2026

Skills

AI EngineersPythonData PreprocessingModel EvaluationStatisticsBigQueryMachine LearningTensorFlowGCPAWS

Key Strengths

Strong academic background in B.Tech Computer Engineering (AI) with a CGPA of 8.48, demonstrating foundational knowledge.
Demonstrated project experience in diverse AI/ML domains, including Natural Language Processing (BioBERT, LLMs, RAG), Computer Vision (CNNs for medical imaging), and traditional Machine Learning (XGBoost, Random Forest for mortality prediction).
Proficiency in key AI/ML frameworks such as PyTorch, TensorFlow, Keras, and Scikit-learn, indicating hands-on experience with modern ML tools.
Experience with end-to-end ML pipeline concepts, including data preprocessing, model training, evaluation, and use of MLOps tools like MLflow for tracking and registry.
Certifications in Machine Learning, Data Structures & Algorithms, and Deep Learning, showing a commitment to continuous learning and skill development.
Clear objective and interest aligned with an AI Engineer role, indicating motivation in the field.

Cultural & Operational Fit

Cultural Fit Analysis

Based on the psychometric test score of 0 out of 500, there is no data to evaluate the candidate's cultural fit. This raises significant concerns regarding their potential alignment with team dynamics and company values.

Soft Skills & Operational Fit

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Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

Key Strengths

Strong academic background in B.Tech Computer Engineering (AI) with a CGPA of 8.48, demonstrating foundational knowledge.
Demonstrated project experience in diverse AI/ML domains, including Natural Language Processing (BioBERT, LLMs, RAG), Computer Vision (CNNs for medical imaging), and traditional Machine Learning (XGBoost, Random Forest for mortality prediction).
Proficiency in key AI/ML frameworks such as PyTorch, TensorFlow, Keras, and Scikit-learn, indicating hands-on experience with modern ML tools.
Experience with end-to-end ML pipeline concepts, including data preprocessing, model training, evaluation, and use of MLOps tools like MLflow for tracking and registry.
Certifications in Machine Learning, Data Structures & Algorithms, and Deep Learning, showing a commitment to continuous learning and skill development.
Clear objective and interest aligned with an AI Engineer role, indicating motivation in the field.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit