Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Роль чётко сфокусирована на современной AI-платформе, с конкретными инфраструктурными задачами и возможностями развития; условия компенсации не указаны.
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Cloud Platform Engineer (AI): Operating and evolving a Kubernetes-based platform for AI workloads, including LLM gateways, agent runtimes, cloud model integrations, and infrastructure as code with an accent on multi-provider routing, observability, security, and GPU-based inference. Focus on designing reliable multi-tenant platform capabilities, implementing GitOps and monitoring workflows, and prototyping self-hosted model serving.
Location: Hybrid, with locations in Charlotte, North Carolina and New York, United States.
Company
hirify.global is a growing public company focused on creating better outcomes for its people and clients.
What you will do
Operate and evolve a Kubernetes-based platform for AI workloads, including LLM gateways, agent and tool-integration gateways, and agent runtimes.
Integrate and manage cloud-hosted foundation models across multiple providers, including routing, quotas, fallbacks, cost controls, and tenant isolation.
Deliver infrastructure as code and GitOps workflows.
Build monitoring, alerting, and tracing for AI workloads, and help design and prototype self-hosted model serving with GPU workloads and inference runtimes.
Harden the platform through secrets management, network policies, authentication and authorization, and audit logging.
Support internal product teams through documentation, onboarding, and troubleshooting.
Requirements
3+ years of experience in platform, DevOps, SRE, or cloud engineering.
Hands-on Kubernetes experience, including deployments, networking, and troubleshooting.
Experience with infrastructure as code using Terraform and GitOps tooling such as Argo CD, Flux, or similar.
Solid experience with at least one major cloud provider: Azure or AWS.
Experience with observability tools such as OpenTelemetry, Prometheus, and Grafana.
Scripting and coding skills in Python, Go, or Bash, plus CI/CD experience.
Nice to have
Experience with LLM gateways or proxies, or LLM observability and tracing tools.
Familiarity with managed model services from major cloud providers such as Azure or AWS.
Understanding of agent frameworks and tool-integration protocols such as Model Context Protocol (MCP).
Experience running GPU workloads or model inference serving.
Experience in multi-tenant, regulated, or compliance-driven environments.
Culture & Benefits
Collaborative, open, and flexible work environment focused on creativity, innovation, teamwork, and professional growth.
Professional development opportunities, including reimbursement for training and continuing education.
Modern open offices with complimentary lunches and fully stocked kitchens.
Wellness programs, flexible time off, and family-formation benefits.
Paid volunteer time off and donation matching.
Hiring process
The hiring process generally includes three or more virtual or in-person meetings.
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