Lately, I’ve been exploring graph-based knowledge representations as a method for information compression and long-term memory permanence in AI systems — developing pipelines that integrate LLMs with structured memory to retain and generalize knowledge efficiently.
My background bridges applied machine learning and software engineering, from building text-to-speech systems and semantic retrieval tools to experimenting with persistent, graph-oriented architectures for reasoning and recall. I’m looking for opportunities at the intersection of AI systems engineering, knowledge representation, and scalable memory architectures.
Remote: Yes
Willing to relocate: Sure
Technologies: Deep Learning (PyTorch), ReactJS, Svelte, Natural Language Processing (NLP), Python (Flask, OpenAI API), Graph Knowledge Systems, Vector Search (Cosine Similarity), PostgreSQL, Linux System Administration
Résumé/CV: https://www.toptal.com/resume/rodrigo-heck
Email: rodrigo.heck@toptal.com
Lately, I’ve been exploring graph-based knowledge representations as a method for information compression and long-term memory permanence in AI systems — developing pipelines that integrate LLMs with structured memory to retain and generalize knowledge efficiently.
My background bridges applied machine learning and software engineering, from building text-to-speech systems and semantic retrieval tools to experimenting with persistent, graph-oriented architectures for reasoning and recall. I’m looking for opportunities at the intersection of AI systems engineering, knowledge representation, and scalable memory architectures.