Senior Infrastructure Engineer
2 semanas atrás
Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration, Workato helps businesses globally streamline operations by connecting data, processes, applications, and experiences. Its AI-powered platform enables teams to navigate complex workflows in real-time, driving efficiency and agility.
Trusted by a community of 400,000 global customers, Workato empowers organizations of every size to unlock new value and lead in today’s fast-changing world. Learn how Workato helps businesses of all sizes achieve more at workato.com.
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
What does our ML/AI team do? The team supported a Generative AI solution for automation and integration.
Our team comprises international senior-level professionals from across the globe, experienced in building high-performing, scalable, enterprise-grade applications. We are a unique team of multinational professionals with integration, cloud, and consumer experience.
We are currently seeking a promising Middle Infrastructure Engineer with experience in the AI domain.
As a Middle Infrastructure Engineer, you will be responsible for deploying, scaling, and maintaining services for the ML/AI team. You will closely work with ML Engineers and Data Scientists as part of a small, flexible team and will have a direct impact on the modernization and maturation of the platform, including infrastructure architecture decisions.
Requirements
Qualifications / Experience / Technical Skills
- BS/MS degree in Computer Science, Engineering or a related subject (or equivalent experience)
- 3-5 years professional experience in hands-on engineering roles (DevOps/SRE)
- 6+ months of experience with hosting AI models (ML flow, AWS SageMaker, Azure AI, Kubernetes) is preferred
- 6+ months of experience with ML Ops (ML flow, vector databases (Qdrant), Dagster) is preferred
- Kubernetes Expertise: Strong experience managing Kubernetes clusters and workloads, specifically using EKS (Elastic Kubernetes Service)
Soft Skills / Personal Characteristics
- Readiness to communicate in English with your colleagues
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