Сильная позиция в растущем AI-домене с четко определенным кругом задач и современным стеком, хотя и подразумевает дежурства по выходным.
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ДежурстваЗарплата не указанаСовременный стекТрендовый доменСильный бэкграундЧеткие задачи
Оценка от Hirify AI
Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Senior Technical Support Engineer (AI): Owning enterprise technical support cases across Kubernetes infrastructure, ML platform components, authentication, data connectivity, and model deployment with an accent on end-to-end troubleshooting and clear customer communication. Focus on diagnosing complex cloud and container issues, coordinating bug resolution with Product and Engineering, and building knowledge resources for globally distributed support operations.
Location: Remote United Kingdom
Company
hirify.global builds an AI platform for highly regulated organizations, combining model and application development, hosting, and governance capabilities.
What you will do
Own enterprise customer support cases from initial triage through resolution across all severity levels.
Diagnose Kubernetes and cloud infrastructure issues, including pod failures, resource limits, persistent volumes, RBAC, ingress, and cluster-level problems.
Troubleshoot ML platform issues involving workspaces, jobs, environment builds, model deployments, and data connectors.
File actionable bug reports and enhancement requests in Jira while advocating for customers with Product and Engineering.
Write and review knowledge base articles, how-to guides, and troubleshooting documentation.
Hand off cases across AMER, EMEA, and APAC, run live customer troubleshooting sessions, and participate in the EMEA weekend on-call rotation.
Requirements
3–5 years of experience in enterprise technical support, solutions engineering, or a similar customer-facing technical role at a SaaS or data/AI platform company.
Hands-on Kubernetes experience, including pod lifecycle, kubectl, RBAC, namespaces, persistent volumes, and cluster troubleshooting.
Strong Linux and command-line skills, including log analysis, process management, filesystem navigation, and shell scripting.
Familiarity with Python-based ML workflows, including Jupyter, package management, model training, and model serving.
Experience with AWS, GCP, or Azure and containerized application environments.
Clear written communication, systematic troubleshooting, ability to manage multiple time-sensitive cases, and a bachelor's degree in computer science, engineering, or a related field, or equivalent experience.
Culture & Benefits
Remote-first work in a globally distributed team.
Asynchronous collaboration across time zones with follow-the-sun case handoffs.
Teaching and learning environment focused on professional growth.
Inclusive workplace that welcomes people from diverse backgrounds and identities.
Participation in a scheduled EMEA weekend on-call rotation.