SKILLS AND EXPERTISE
How I Add Value
With a focus on Machine Learning, Data Science, AI Engineering, and technical leadership, I build intelligent systems that turn complex challenges into reliable, production-ready solutions.
Machine Learning & Data Science
- Developed and optimized classical machine learning models for classification, regression, and clustering, achieving high accuracy in predictive analytics and decision-support systems.
- Proficient in the Python Data Science stack, including pandas, NumPy, matplotlib, and seaborn, for data wrangling, exploratory data analysis (EDA), and model pipeline development.
- Enhanced model reliability through meticulous data preprocessing, cleaning, and feature engineering, ensuring robust and high-quality inputs.
- Built interactive dashboards and visualizations to communicate data-driven insights to both technical and non-technical stakeholders.
- Applied statistical techniques such as regression, hypothesis testing, A/B testing, and performance evaluation metrics to validate model results and guide data-driven decisions.
- Designed and trained custom neural networks (MLPs, CNNs) for object detection, facial recognition, and medical image analysis using PyTorch, TensorFlow, OpenCV, and YOLO.
- Fine-tuned transformer models such as DeBERTa and MiniLM for sentiment analysis, emotion detection, and text classification, deploying them via Render and integrating into production-grade web applications.
AI Engineering
- Built agentic AI systems and multi-step LLM workflows with LangChain, LangGraph, and n8n, orchestrating tool use, memory, and structured outputs for autonomous task execution and automated reporting pipelines.
- Designed and deployed Retrieval-Augmented Generation (RAG) pipelines backed by vector stores, pairing semantic retrieval with LLM reasoning for grounded, context-aware responses.
- Integrated frontier LLMs across providers — Anthropic Claude, Google Gemini, and OpenAI — through their SDKs, shipping production features with streaming, function/tool calling, and structured JSON outputs.
- Applied prompt-engineering and LLM-orchestration patterns — context/memory management, guardrails, and output evaluation — to improve reliability and reduce hallucination in production systems.
- Deployed and served models on cloud AI infrastructure (AWS & Databricks), handling authentication, scaling, and cost-aware inference.
Project Management & Leadership
- Led and coordinated multidisciplinary teams through the planning, analysis, design, and implementation phases of multiple software development projects, ensuring on-time and high-quality delivery.
- Competed in national programming competitions and hackathons including ASEAN AI Hackathon, Ship or Be Shipped 2025, Byte Forward Hackathon 2025, DLSU Hackercup, and CodeChum National Programming Challenge 2024, successfully managing high-pressure deadlines and technical presentations.
- Organized and facilitated programming workshops and mentored Computer Science students under the Mapúa MCL-ACM Student Chapter, fostering collaboration and technical growth within the academic community.