Professional Objective
Motivated aspiring Full-Stack AI Engineer and Data Analyst seeking roles to apply TensorFlow, PyTorch,
and Python data pipelines (Pandas/NumPy) to real-world complexities. Highly skilled at training machine
learning models, driving data-informed decisions, and validating modern AI systems using metrics like
Accuracy, F1, ROC-AUC, Contextual Relevance, Groundedness, and Hallucination Detection.
Technical Skills
Languages: Python, SQL, TypeScript, R
Deep Learning: TensorFlow, PyTorch, Keras
Machine Learning: Scikit-learn, NLP (NLTK), CV (OpenCV)
Data Analytics: Pandas, NumPy, Matplotlib, Seaborn
AI Metrics: Accuracy, F1, ROC-AUC, Groundedness, Hallucination Detection
DevOps & DB: Docker, PostgreSQL, MySQL
Education
Associates Degree in Computer Technology
AMA Computer Learning Center, Balanga City, Bataan
Expected Graduation: December 2026
Relevant Coursework: Database Systems,
Web Development, Software Engineering
Certifications & Training
Machine Learning & Data Science | Self-Study | Feb 2026
Completed 100+ hours covering Scikit-learn, model training,
and data visualization (Matplotlib/Seaborn).
Deep Learning Specialization | Self-Study | Mar 2026
Mastered neural network architectures using TensorFlow,
PyTorch, and Keras for vision and NLP tasks.
AI Diagnostics & Validation | Self-Study | Apr 2026
Focused on model evaluation (ROC-AUC, Precision, Recall) and
GenAI metrics (Groundedness, Hallucination Detection).
Additional Skills
Version Control: Git, GitHub, Branching Strategies, Pull Requests
Collaboration Tools: Slack, Microsoft Teams, JIRA, Agile Methodologies
Languages: English (Fluent), Filipino (Native)
Soft Skills: Team Collaboration, Self-Directed Learning, Problem Solving,
Time Management
Portfolio Projects
NeuroVision Analytics Platform
Python, PyTorch, OpenCV, NumPy | Real-Time Anomaly Detection
- Engineered a real-time computer vision pipeline using OpenCV and
PyTorch, achieving 95%+ Accuracy and high F1-score in visual anomaly
classification.
- Preprocessed unstructured image streams into structured datasets utilizing
NumPy and Pandas for optimized deep learning model
ingestion.
- Validated model diagnostics over 10,000+ images, optimizing the Precision-Recall
tradeoff to minimize critical false negatives in production environments.
Nexus NLP Intelligence Engine
Python, TensorFlow, NLTK, Pandas | Semantic RAG Search
- Architected an enterprise NLP engine combining TensorFlow and
NLTK to process, embed, and query large-scale unstructured document
databases.
- Pioneered modern Generative AI validation pipelines, specifically deploying Contextual
Relevance and Groundedness metrics to filter out 99% of AI Hallucinations.
- Analyzed prompt interactions and sequence model confidence scores via
Pandas and Seaborn, driving continuous improvements in
retrieval accuracy.
Predictive Churn & Customer Analytics
Python, SQL, Scikit-learn, Seaborn | Data Science Pipeline
- Developed an end-to-end predictive analytics pipeline utilizing SQL,
Pandas, and Scikit-learn to forecast customer churn
with high predictability.
- Conducted extensive Exploratory Data Analysis (EDA) visualized via
Seaborn and Matplotlib, uncovering critical user
behavioral trends and flight risks.
- Trained and optimized classification models achieving an outstanding ROC-AUC score,
directly informing targeted retention strategies and minimizing false positives
(Precision).
Key Achievements
- Completed intensive 12-week self-study program in complete AI/Data ecosystems (250+ hours).
- Designed, trained, and deployed multiple machine learning models from scratch.
- Targeted high Validation Accuracy, minimizing Hallucinations/False Positives across outputs.
- Successfully automated data pipelines utilizing the robust Python data stack (Pandas, SQL).
- Published production-ready AI/Data projects on GitHub with comprehensive documentation.
- Active contributor to open-source Python projects and developer communities.