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AI Engineer with less than a year in Python, NLP, and MLOps.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Highly motivated BS Computer Science student with a solid foundation in machine learning, deep learning, and natural language processing. Experienced in building end-to-end ML systems, developing Flask APIs for real-time predictions, and implementing MLOps best practices. Proficient in Python, Scikit-learn, TensorFlow/Keras, and Docker, with a keen interest in AI and data science applications.
Institute of Business Administration
BS Computer Science · Computer Science
August 5, 2022 – Present
Phishing Detection System
July 5, 2025 – July 9, 2025
Built an end-to-end ML system to detect phishing URLs following an industry-standard project structure. Implemented data preprocessing, feature engineering, model training, and validation pipelines. Added data drift detection and exception logging to ensure model reliability in production. Designed automated ML pipelines for training and prediction workflows. Developed a Flask API to serve real-time phishing detection predictions. Ensured scalability and production readiness using MLOps best practices.
View ProjectHate Speech Detection
April 27, 2025 – April 27, 2025
Built an NLP-based machine learning model to detect and classify hate speech from text data. Performed text preprocessing including tokenization, stopword removal, stemming/lemmatization, and vectorization using Word2Vec. Designed the pipeline from data cleaning to model evaluation for real-world text moderation use cases. Demonstrated practical application of NLP techniques for automated content filtering.
View ProjectSupervised Machine Learning: Regression and Classification
DeepLearning.AI
June 13, 2025 – Present
Python for Data Science, AI & Development
IBM
February 19, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a focus on practical applications of AI/ML, which aligns with an engineering mindset. The diversity of projects (phishing detection, hate speech detection) shows a breadth of interest within the AI domain. However, as an entry-level candidate with only academic projects, the depth of exposure to diverse team environments and corporate culture is limited. The target role of 'AI Engineer' is well-aligned with the skills and projects presented.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a structured approach to problem-solving and an understanding of production-readiness for ML systems. The academic nature of projects suggests a strong learning aptitude. However, without direct work experience or psychometric test results, it's difficult to assess collaboration, stress handling, or work attitude.