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Arize AI

AI model observability and evaluation platform

DevOps Support Engineer

Customer SupportConfirmed live in the last 24 hours
Full-timeBuenos Aires, ArgentinaNo salary listed

About the Job

Requirements

  • At least 3 years of experience in a DevOps, infrastructure, or technical support role.
  • Basic working knowledge of Kubernetes, including reading logs, describing resources, and understanding pod and service behavior.
  • Familiarity with at least one major cloud provider: AWS, GCP, or Azure.
  • Ability to systematically diagnose issues.
  • Strong written and verbal communication skills in English.
  • Eagerness to learn in a fast-paced environment with a high degree of autonomy.

Responsibilities

  • Serve as the first point of contact for on-premises customer issues by triaging and resolving infrastructure and platform support requests.
  • Monitor customer platform health using existing observability tools.
  • Investigate and troubleshoot Kubernetes-based deployments to identify root causes, apply fixes, and escalate when needed.
  • Work hands-on with self-hosted environments to diagnose configuration, networking, and performance issues.
  • Document common issues and resolutions to build internal runbooks and a knowledge base.

Desired Qualifications

  • Experience working with or supporting enterprise customers.
  • Familiarity with Helm charts and their use in managing Kubernetes deployments.
  • Experience with networking concepts such as DNS, TLS, proxies, or firewalls.

About the Company

Arize AI

AI model observability and evaluation platform

Arize AI provides a platform for AI observability and LLM evaluation that helps top AI teams monitor and improve models across NLP, computer vision, and recommender systems. It works by offering analytics, LLM traces, spans, RAG analysis, and task-based evaluations to troubleshoot issues and measure performance. It differentiates itself by delivering end-to-end observability and evaluation in a single platform with embedding visualization for RAG to pinpoint missing or irrelevant context. Its goal is to help AI teams ensure their models perform optimally and continuously improve their AI systems.

Company size
201-500
Company stage
Acquired
Total funding
$131M
Headquarters
Berkeley, CA
Founded
2020

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