Staff/Sr. ML Infrastructure / Platform Engineer

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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 QualificationsModel 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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