Novo Brasil, Goiás, Brazil
I am an AI Developer focused on designing and implementing secure, production-ready Generative AI systems across various cloud environments. I specialize in integrating robust RAG (Retrieval-Augmented Generation) pipelines and Agentic Systems that enhance data accuracy and automation. Key Expertise & Technical Stack: AI Engineering & Validation: Proficient in Prompt Engineering and developing advanced RAG solutions. Successfully implemented a TQI validation layer to mitigate hallucination, ensuring data integrity and compliance. MLOps & Deployment: Experience with MLOps and deployment of models using tools like MLFlow across different platforms. Certified in Azure AI Fundamentals and skilled in Versionamento de modelos. Multi-Cloud & Infrastructure: Hands-on knowledge of AWS (EC2, AMI, EBS), Google Cloud AI (Vertex AI, GenAI Introduction), and Azure (Vms, AzureML). Expertise in automating infrastructure via AWS CLI and scripts. Seeking remote Contract/Freelance roles in AI Solutions Engineering where I can leverage Multi-Cloud and MLOps expertise to build the next generation of reliable AI applications. Sou Desenvolvedor de IA, especializado em desenvolver sistemas GenAI, RAG e Agentes de IA seguros e escaláveis em ambientes Multi-Cloud (AWS, Azure, Google Cloud). Domino MLOps e Automação de Infraestrutura para garantir a confiabilidade das soluções. Aberto a propostas de freelancing no Brasil e exterior.
Developed a Proof of Concept (PoC) for an Agentic RAG System where I implemented a proprietary validation layer. This system significantly mitigates model hallucination, ensuring data accuracy and compliance for enterprise applications. Prototypes developed in Java (for initial robustness proof) with a clear focus on migration and future solutions using Python (LangChain, LlamaIndex) for optimal GenAI pipeline flexibility. Hands-on experience with the AWS ecosystem (EC2, AMI, EBS Snapshots) and automation via AWS CLI and Python's Boto3 SDK. Mastered resource lifecycle management (creation, backup, restoration, and cleanup), ensuring solutions are scalable, cost-effective, and fully recoverable (fast disaster recovery) using AMI and Snapshot best practices.