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Technical Support Engineer (Inference) - US Weekends
IT Support / Helpdesk
• Remote
• Full-time
•
EU/EMEA
Provide technical support to Together AI customers building training, fine-tuning, and inference solutions on GPU clusters running on Kubernetes.
Responsibilities
- ▹Engage directly with customers to resolve complex technical challenges involving GPU clusters and inference/fine-tuning services
- ▹Act as a customer-facing SRE ensuring inference endpoints running on Kubernetes remain healthy, stable, and performant
- ▹Become a product expert in Gen AI solutions, serving as the last line of technical defense before Engineering and Product escalation
- ▹Assist with hardware and platform migrations by validating system health and traffic routing
- ▹Monitor dashboards to detect anomalies and escalate with data-backed analysis
- ▹Manage customer-facing communications during incidents, translating technical findings into clear, evidence-backed updates
- ▹Execute infrastructure changes via pull requests (infra-as-code) for endpoint configuration, model bringup/bringdown, and capacity scaling
- ▹Flag engine-level bugs with logs and reproduction steps for engineering
- ▹Collaborate across Engineering, Research, and Product teams to address customer concerns
- ▹Identify patterns in support cases to help drive the product roadmap
- ▹Maintain detailed documentation of system configurations, procedures, and troubleshooting guides
- ▹Provide flexible support coverage during holidays, nights, and weekends
Requirements
- ▹6+ years of experience in a customer-facing technical role, SRE, DevOps, or infrastructure engineering, with at least 1 year supporting an AI service
- ▹Experience as an SRE or DevOps engineer working with Kubernetes
- ▹Strong technical background with knowledge of AI, ML, and GPU technology
Soft skills
Customer-facing communicationCross-team collaborationFlexibility around weekend/on-call scheduling
About the company
Together AI is a pioneering AI company providing training, fine-tuning, and inference solutions to customers on GPU clusters.
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