I'm an ML Infrastructure Engineer specializing in the platform and DevOps layer that makes machine learning viable at scale. At Cognitive Network Solutions, I designed and deployed multi-cloud infrastructure across GCP and Azure, built Kubernetes platforms for reproducible ML service delivery, and engineered CI/CD pipelines that embedded security and least-privilege IAM practices from the ground up.
I've worked hands-on with GPU-accelerated ML workflows using TensorFlow, PyTorch, and MLflow, building the deployment and observability infrastructure that keeps inference systems reliable in production. My focus has consistently been on the operational layer: provisioning cloud resources with Terraform, enforcing access controls, and establishing monitoring stacks that surface issues before they become incidents.
Previously at Dfinitiv, I built cloud-native data pipelines on AWS and GCP to automate media asset workflows, cutting processing time by over 60%.
I'm drawn to the infrastructure side of ML because it's where reliability is actually built. Models are only as good as the systems that serve them.