Workato is hiring a
Senior Infrastructure Engineer

Summary

Join Workato as a Senior Infrastructure Engineer and be part of a small, flexible team that closely works with ML Engineers and Data Scientists to deploy, scale, and maintain services at the ML/AI team. As a key member, you will have a direct impact on the process of modernization and maturation of the platform, including infrastructure architecture decisions.

Requirements

• 7+ years professional experience in hands-on engineering roles (DevOps/SRE)
• BS or MS in the CS field (or equivalent experience)
• 1+ year of experience with hosting AI models
• 1+ year of experience with ML Ops
• Kubernetes Expertise: Strong experience managing Kubernetes clusters and workloads, specifically using EKS (Elastic Kubernetes Service)
• Programming Skills: Proficiency in Python language is essential
• CI/CD Tools: Experience creating scalable development and integration pipelines using CI/CD tools such as GitHub Actions, Argo Workflows, GitLab CI, or similar solutions
• Kubernetes Deployments: Expertise in deploying Kubernetes-based services using tools like Kustomize, Helm, ArgoCD, or similar, following a GitOps approach
• Cloud Architectures: Hands-on experience with cloud-based architectures, particularly Amazon Web Services (AWS)
• Networking and Web Services: Strong understanding of networking fundamentals and web services architecture
• Infrastructure as Code: Experience managing complex infrastructure, including Kubernetes clusters, VPC networking, and security policies, using Infrastructure as Code tools like Terraform
• Containerization: Hands-on experience with containers, including creating and optimizing Dockerfiles, using docker-compose, and a deep understanding of related technologies (kernel features, container networking, image structure, and registry)
• Software Packaging and Testing: Familiarity with software packaging tools, functional testing, security validation tools and services, and code coverage tools
• Compliance and Security: Operating Kubernetes clusters in compliance-regulated environments and understanding kernel-level container technologies such as seccomp, namespaces, and cgroups
• Security Regulations and Compliance: Experience with cloud and infrastructure security regulations and compliance programs, including SOC2, ISO27001, HIPAA, GDPR, and CCPA
• Good communication and collaboration skills in international technological companies
• Readiness to work remotely with teams distributed across the world and timezones
• Good Spoken English to participate in product-related, architectural and technical discussions

Responsibilities

• Deploying, scaling, and maintenance of services at the ML/AI team
• Closely working with ML Engineers and Data Scientists as a part of a small, flexible team
• Having a direct impact on the process of modernization and maturation of the platform, including infrastructure architecture decisions