Michael Olmos Trujillo 👋

Mathematician & Technical Lead | Bridging Advanced Analytics with MLOps Architecture With a Master’s in Computational Modeling and a strong foundation in Mathematics, I specialize in translating complex theoretical concepts into scalable, production-grade AI solutions. Over the last 4+ years, I have transitioned from building models to architecting end-to-end data ecosystems, integrating Data Science with robust MLOps methodologies. My focus is on technical leadership: ensuring that machine learning initiatives are not just accurate, but reproducible, scalable, and impactful. Key Technical Achievements: MLOps & System Design: Architected iterative model lifecycles (training, tracking, deployment) using MLflow, Docker, and Cloud Infrastructure (AWS/GCP). Predictive Analytics: Led the development of advanced algorithms for government-funded projects, utilizing Scikit-learn and imbalanced dataset techniques to outperform legacy metrics and identify project risks. Full-Stack Data Integration: Engineered optimized ETL pipelines (PHP/Laravel) and interactive front-ends (Streamlit) to democratize data access for stakeholders. Unsupervised Learning: Deployed clustering algorithms to segment healthcare recovery patterns in Colombia, driving data-informed decision-making. Tech Stack: Core & AI: Python (PyTorch, Scikit-learn, PySpark, NLTK, SpaCy, LangChain), R. Infrastructure: Docker, AWS, GCP, MLflow. Visualization: Power BI, Tableau, Streamlit. I am driven to lead technical teams in developing Multi-agent systems, LLMs, and Deep Learning solutions. My mission is to leverage cutting-edge technology to solve human-centric problems and reduce environmental impact.