Senior Infrastructure Engineer (MLOps)

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Requirements:

• 7+ years of professional experience in hands-on engineering roles (DevOps/SRE), with a BS or MS in Computer Science (or equivalent).
• 1+ year of experience with hosting AI models (ML flow, AWS Sagemaker, Azure AI, Kubernetes).
• 1+ year of experience with ML Ops (ML flow, vector databases, dagster).
• Strong experience managing Kubernetes clusters and workloads using EKS.
• Proficiency in Python; knowledge of Go, Ruby, or JavaScript is a plus.
• Experience with CI/CD tools like GitHub Actions or GitLab CI.
• Expertise in deploying Kubernetes-based services using Kustomize, Helm, and GitOps tools.
• Hands-on experience with AWS architectures, networking fundamentals, and web services.
• Experience using Infrastructure as Code tools like Terraform.
• Knowledge of container technologies and best practices.

Responsibilities:

• As a Senior Infrastructure Engineer, you will be responsible for deploying, scaling, and maintaining services at the ML/AI team.
• You will work closely with ML Engineers and Data Scientists as part of a flexible team.
• Your role will have a direct impact on the modernization and maturation of the platform, including infrastructure architecture decisions.