Jasleen Kaur Matharoo

AI Engineer

https://www.opentalent.in/jasleen-kaur-matharoo

RAIPUR, indiaMember since September 22, 2025

Key Strengths

Strong foundational knowledge in AI/ML, evidenced by a high score in the Data Scientist — Artificial Intelligence assessment.
Practical experience with several AI/ML projects, including chatbots, predictive models, and image generation.
Proficiency in key AI development technologies such as Python, PyTorch, Hugging Face Transformers, and Flask.
Demonstrated ability to apply AI to real-world problems in areas like women's safety, healthcare, and customer analytics.
Background in ethical hacking suggests an awareness of security considerations, which can be valuable in AI system design.

Skill Analysis

Data Science & Artificial Intelligence

★★★★★

94%

Achieved a very high score (94%) in the Data Scientist — Artificial Intelligence exam, indicating strong foundational and applied knowledge in the core areas relevant to an AI Engineer role.

Data ScientistMachine LearningDeep LearningPredictive ModelingNatural Language ProcessingComputer VisionPythonPyTorchHugging Face TransformersFlask

Strengths

Demonstrated strong theoretical and practical understanding of core AI/ML concepts.
Proficient in applying data science methodologies to solve complex problems.
Achieved high accuracy in AI-specific tasks, indicating strong domain knowledge.
Experience with modern AI tools and frameworks like PyTorch and Hugging Face.

Limitations

Insufficient data to identify specific weaknesses within this high-scoring test; performance was consistently strong.

Cultural & Operational Fit

Cultural Fit Analysis

The psychometric test score of 41% suggests potential challenges in logical reasoning, stress handling, and team collaboration. This indicates a moderate to low cultural fit based on these specific metrics.

Soft Skills & Operational Fit

Communication clarity and professional language usage are moderate (65%). Logical reasoning, work attitude, stress handling, and team collaboration are below average (41%), indicating potential areas for development in operational fit.

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