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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