Staff/Sr. ML Infrastructure / Platform Engineer

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趨勢科技 - 全球雲端資安領航者 / 全亞洲最大軟體公司 / 企業版圖橫跨五大洲 / 趨勢全球研發基地在台灣
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About the Role
We are building a production-grade, GPU-accelerated LLM serving platform that powers multiple AI products at enterprise scale. You will be responsible for designing, building, and operating the infrastructure that serves large language models — from raw Kubernetes cluster management to multi-GPU inference optimization and autoscaling.
Required Qualifications
Model Serving & Inference
Operate multi-model LLM serving infrastructure
Tune autoscaling policies to balance GPU cost and latency SLAs
Kubernetes & GPU Infrastructure
Operate production K8s clusters with NVIDIA GPU nodes
Handle GPU node lifecycle: NVIDIA driver setup
Infrastructure as Code
Write and maintain Terraform/Terragrunt modules for AWS/GCP cloud
Package platform components and model deployments as Helm charts
Manage multi-environment configurations
Observability & Performance
Maintain monitoring stack: Prometheus, Grafana,
Build dashboards for GPU utilization, KV cache occupancy, TTFT/ITL latency, and cost per token
Set up alerting for SLA violations and OOM events
Bonus Skills
These are not required, but candidates with these skills will stand out.
LoRA / PEFT fine-tuning workflows
MLflow for experiment tracking, model registry, and automated adapter deployment
Experience building LoRA adapter CI/CD pipelines (training → registry → serving)
Experience with alternative inference frameworks such as SGLang or NVIDIA NIM, including deep Parameter Tuning for Continuous Batching, KV Cache management, and Speculative Decoding.
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