I am a Machine Learning Engineer with over five years of experience designing and implementing scalable data infrastructure that empowers data scientists to deploy machine learning models efficiently. My expertise lies in building robust ML pipelines and optimizing model deployment on platforms such as AWS, Databricks, and Vertex AI.

With a strong focus on MLOps, I integrate CI/CD/CT practices to ensure the reliability, scalability, and maintainability of machine learning systems in production. My technical stack includes Python, MLflow, Kubeflow, Kubernetes, and cloud-native solutions for end-to-end model lifecycle management.

In addition to traditional ML, I specialize in Generative AI, applying best practices in model versioning, deployment pipelines, and performance monitoring to deliver cutting-edge yet scalable AI solutions.

Backed by a Master’s degree in Computer Science, I combine a theoretical foundation with practical experience to solve complex problems, drive data-driven decision-making, and contribute to AI advancements.

• Studying Transformer models and Classic neural networks
• Exploring Golang, Python, and Java
• Engaging with the tech community through knowledge-sharing

I’m always open to collaborations, discussing ML, MLOps, and AI trends, or even chatting about music, games, and programming.

🚀 Let’s shape the future of AI together!

🔗 Connect with me on LinkedIn: Diego Silva de Salles