Buenos Aires, Buenos Aires Province, Argentina
I'm a Machine Learning Engineer based in Buenos Aires with 8+ years building production AI systems for LatAm, US and European companies — remotely. My focus is multimodal AI: extracting insights from audio, video, image, and text at scale. I've worked across the stack — from fine-tuning state-of-the-art models to deploying them on cloud infrastructure — for startups in music tech, ad analytics, and speech synthesis. What I do: Audio & speech ML (TTS, voice conversion, music analysis) Video & image understanding (ad detection, content analysis) LLM integration and NLP pipelines End-to-end deployment (AWS, GCP, FastAPI, Docker) How I work: I take on part-time contractor engagements — typically async, delivery-focused, with no need for daily standups or fixed hours. I work well with small technical teams that need senior ML capacity without a full-time hire. Open to: contractor roles, part-time engagements, and technical consulting.
I focus on automating the extraction of valuable insights from various media formats, including audio, text, video, and images. By developing advanced algorithms, I enable the efficient analysis of ads across podcasts, videos, and social networks, optimizing the way we derive information and generate value.
I specialized in cutting-edge singing synthesis and voice conversion, leveraging state-of-the-art models and implementing them on scalable platforms like covers.ai. My role involved bringing these advanced models into the real world to enhance the music production process.
During my tenure at FakeYou.com, I automated dataset creation tasks and pioneered a novel approach to fine-tuning text-to-speech models. By adapting the grapheme-to-phoneme system, I enabled seamless integration of these models into new languages, eliminating the need for training from scratch. This breakthrough significantly streamlined the development of multilingual speech synthesis systems
Utilizing state-of-the-art Music Information Retrieval (MIR) models, I spearheaded the development of advanced machine learning pipelines for a cutting-edge music analytics platform. These pipelines enabled the extraction of valuable insights and analysis of music data, empowering users to make data-driven decisions in areas such as recommendation systems, genre classification, and music similarity.
I worked preparing the material and teaching about web scraping and text generation with deep learning models. The classes are open source, they are available here: https://github.com/institutohumai/cursos-python