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AI Engineer with less than a year in Machine Learning & Data Analysis
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
An accomplished data scientist with a strong foundation in data analysis, predictive modeling, and data-driven strategy development. Proven track record of extracting actionable insights from complex datasets and implementing data-driven solutions to solve real-world business problems. Proficient in programming languages such as Python and experienced in data manipulation using SQL.
LOYOLA ACADEMY
MSc · Data Science
October 1, 2022 – July 1, 2024
LOYOLA ACADEMY
BSc · Computer Science & Engineering
June 1, 2019 – May 1, 2022
INTEQ Software Private Limited
Machine Learning & Language Translation
March 1, 2024 – May 1, 2024
India
IKEA Product Price Prediction Using Machine Learning
June 21, 2026 – Present
Collected and Cleaned product data (name, category, size, material, etc.) to build a predictive pricing model. Trained multiple regression models (Linear Regression, Random Forest, XGBoost), achieving an RMSE. Evaluated models with cross-validation.
Flower Type Classification And Detection System Using Deep Learning
June 21, 2026 – Present
Built a deep learning system to classify and detect flower species using CNNs and transfer learning. Achieved over 90% accuracy on the Flower dataset; Applied image preprocessing and augmentation techniques to improve model robustness and generalization. Developed a simple user interface for real-time predictions and bounding box visualization. Deployed models using TensorFlow and PyTorch.
Full Stack Data Science & AI.
Unknown
June 1, 2026 – Present
NPTEL Certificate on Python for Data Science.
NPTEL
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a focus on practical applications of AI/ML, which aligns with an AI Engineer role. The diversity of projects (image classification, price prediction, language translation) indicates a broad interest in different AI domains. However, the experience is primarily academic and internship-based, suggesting a need for more exposure to industry best practices, collaborative development environments, and agile methodologies to fully integrate into a senior-level engineering culture. The lack of open-source contributions or team-based project descriptions limits the assessment of collaborative cultural fit.
Soft Skills & Operational Fit
The candidate's profile indicates a foundational understanding of data science workflows, from data collection and preprocessing to model deployment. The academic projects showcase initiative and practical application of learned concepts. The internship provides exposure to real-world ML development. However, the psychometric test score is not provided, making it difficult to assess logical reasoning, work attitude, stress handling, and team collaboration. The English test score of 58 suggests moderate communication clarity, which might impact operational fit in roles requiring extensive documentation or client interaction.