I’m an ML DevOps Engineer with hands-on experience designing and deploying multi-cloud AI infrastructure across GCP and Azure. At Cognitive Network Solutions, I build and maintain Kubernetes-based platforms for telecom AI systems: covering everything from Terraform automation and GPU orchestration to MLflow tracking, Neo4j graph databases, and Kafka event streaming.
Previously, I interned at Dfinitiv where I developed Python-based automation tools and AWS-native backend services for media curation workflows. Across both roles, I’ve focused on building systems that are scalable, reliable, and easy for teams to operate in production.
My interests sit at the intersection of AI infrastructure, automation, and graph-driven intelligence. I enjoy solving complex problems end-to-end—from model deployment pipelines to distributed data systems: and I’m always looking for new ways to make technology faster, cleaner, and more collaborative.
Outside of engineering, I value teamwork, curiosity, and strong communication. I thrive in environments where ideas move quickly and engineers work closely to bring ambitious systems to life.