Senior Technical Lead
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Role details
Tech stack
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Job description
Design and operate large-scale AI infrastructure for model serving, GPU resource management, workload scheduling, autoscaling, and SLA-based operations - the platform backbone that inference and agentic workloads run on.
Requirements
10+ yrs Cloud & Distributed Systems Ā· 3+ yrs AI Infrastructure Ā· Production GPU cluster experience, Must have Kubernetes & Cloud-Native Architecture GPU Infrastructure (H100/H200, CUDA, MIG) LLM Serving Platforms (vLLM, KServe) GPU Scheduling & Resource Management Distributed Systems Architecture Autoscaling, Routing & Capacity Optimization SRE, Observability & Performance Engineering
Must Have Skill
GPU,Kubernetes,Large Language Models (LLMs)
Good to Have Skill
Distributed Computing
Other Requirements
GPU Infrastructure - H100/H200, CUDA, MIG partitioning LLM Serving Platforms - vLLM, KServe for model serving at scale Distributed Systems Architecture - Scheduling, autoscaling, capacity & routing SRE & Observability - SLA-based operations, performance engineering Cloud-Native Foundation - Kubernetes & cloud-native architecture
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Prepare application
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