About WorkatoWorkato makes the creation and implementation of automations 10X faster than traditional platforms. As the leader in AI-powered enterprise automation, we enable enterprises to automate their business processes across the organization by integrating their applications, data, and experiences.Job DescriptionWorkato 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.Why join us?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!Responsibilities As 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.Requirements Qualifications / 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).Programming Skills: Proficiency in Python language is essential. Ability to program in other languages such as Go, Ruby, or JavaScript is a plus.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).#J-18808-Ljbffr