I'm in the final year of a five-year degree in Artificial Intelligence Engineering at University of San Andrés, graduating at the end of this year (2026). With experience as an AI & Agentic RAG Engineer and Robotics Researcher, I focus on building production-ready agentic architectures and autonomous systems. Currently open to Machine Learning Engineering and Applied AI opportunities.
AI & Agentic RAG Engineer — Operations & Support Copilot
SLAM & ROS2 Researcher & Developer
Final Reinforcement Learning course project where we implemented PPO from scratch, validated it on CartPole-v1 (mean return 500), and trained it on racetrack-v0 (highway-env). It includes custom PPO vs SB3 comparisons and demo rollouts showing stable lane-following with smoother steering.
End-to-end AI platform for CV understanding, job opportunity matching, and recruiter-side candidate discovery. Powered by a custom fine-tuned Qwen model on Google Cloud, FastAPI, Next.js, and Supabase, featuring hybrid search and fallback ranking under high traffic.
Reproducible inventory decision prototype built on the public Walmart M5 dataset. It combines probabilistic LightGBM forecasts, (s,S) and MILP replenishment policies, and 200 SimPy replications. In the documented simulation, MILP reached a 98.10% fill rate and reduced expected cost by 36.66% versus the baseline under synthetic costs and lead times.
Machine Learning project to estimate used SUV prices from 22,377 listings. I trained and tuned four regression models through Azure Machine Learning jobs and datasets on dedicated compute clusters; Random Forest achieved the best test metrics (RMSE 7656.09, MAE 4784.67), with mileage and vehicle year analyzed as key price drivers.
A real-time global click counter. Completely pointless, oddly satisfying.
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