Staff/Sr. ML Infrastructure / Platform Engineer
Trendmicro Open · verified Oct 22, 2026 At a glance
Senior ML Platform & Ops role at Trendmicro. Taipei · full-time.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Run multi-model LLM serving and adjust autoscaling to manage GPU expense alongside latency targets
- Operate Kubernetes clusters with NVIDIA GPUs, including setting up drivers and managing node lifecycles
- Create and maintain Terraform or Terragrunt modules, Helm packages, and configurations for different environments
- Track GPU use, KV cache occupancy, latency and token costs, then alert on SLA breaches and out-of-memory events
What you bring
- Hands-on experience operating production Kubernetes clusters with NVIDIA GPU nodes
- Experience maintaining Terraform or Terragrunt modules and packaging deployments as Helm charts
- Experience monitoring GPU infrastructure with Prometheus and Grafana
Who this fits
This senior infrastructure role suits you if you want to build and operate GPU-backed LLM serving systems. It is based in Taipei, Taiwan.
From the employer
Join Trend ‧ Join New Generation
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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