.Workato is the only integration and automation platform that is as simple as it is powerful — and because it's built to power the largest enterprises, it is quite powerful.Simultaneously, it's a low-code/no-code platform. This empowers any user (dev/non-dev) to painlessly automate workflows across any apps and databases.We're proud to be named a leader by both Forrester and Gartner and trusted by 7,000+ of the world's top brands such as Box, Grab, Slack, and more. But what is most exciting is that this is only the beginning.Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.But, we also believe in balancing productivity with self-care. That's why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.If this sounds right up your alley, please submit an application. We look forward to getting to know you!ResponsibilitiesAs a Senior Infrastructure Engineer, you will be responsible for deploying, scaling, and maintenance of services at the ML/AI team. You will closely work with ML Engineers and Data Scientists as a part of a small, flexible team and will have a direct impact on the process of modernization and maturation of the platform including infrastructure architecture decisions.What does our ML/AI team do? The team supported Generative AI solution for automation and integration.Our Team International Sr-level professionals from all over the Globe are experienced in building high-performing, scalable, enterprise-grade applications.RequirementsQualifications / Experience / Technical Skills7+ 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 (ML flow, AWS Sagemaker, Azure AI, Kubernetes).1+ year of experience with ML Ops (ML flow, vector databases (qdrant), Dagster).Kubernetes Expertise: Strong experience managing Kubernetes clusters and workloads, specifically using EKS (Elastic Kubernetes Service).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
