Senior Infrastructure Engineer (ML/AI)

About Workato
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.

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.

We also believe in balancing productivity with self‑care. That’s why we offer a vibrant and dynamic work environment along with a multitude of benefits that employees can enjoy inside and outside of their work lives.

Featured recognitions:

• Business Insider named us an “enterprise startup to bet your career on”
• Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world
• Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
• Quartz ranked us the #1 best company for remote workers

Responsibilities

As a Senior 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.

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 hosting AI models (ML flow, AWS SageMaker, Azure AI, Kubernetes) is preferred
• 6+ months of experience with ML Ops (ML flow, vector databases such as Qdrant, Dagster) is preferred
• Strong experience managing Kubernetes clusters and workloads, specifically using EKS (Elastic Kubernetes Service)
• Proficiency in Python; knowledge of Go, Ruby, or JavaScript is a plus

Soft Skills / Personal Characteristics

• Readiness to communicate in English with colleagues

Seniority Level: Mid‑Senior level
Employment Type: Full‑time
Location: Berlin, Germany

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