I am an AI Engineer and Data Scientist experienced in designing, developing, and deploying Machine Learning solutions in production. My main focus is bridging the gap between data science and software engineering by building scalable, resilient, and efficient AI architectures.
In my professional experience, I have built real-time hybrid recommendation engines (leveraging Transformer and Two-Tower architectures), RAG systems, and conversational AI voice assistants. I specialize in taking models from experimentation to high-availability production environments using tools like NVIDIA Triton Inference Server, vector databases (Qdrant), and orchestrating microservices.
Currently, I am expanding my expertise in MLOps and Cloud Architecture through the advanced "Talento Tech" bootcamp (MinTIC), focusing on deployments with Kubernetes, AWS EKS, Terraform (IaC), and Docker. Additionally, I am pursuing the IBM AI Engineering Professional Certificate to further solidify my technical profile.
I am passionate about optimizing processes through Artificial Intelligence and am always looking for new challenges where I can contribute by building robust, production-ready Machine Learning ecosystems.
