MLOps Engineer: GPU-Driven LLM Infrastructure Lead

Trine Infotech; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Trine Infotech is seeking an experienced MLOps Engineer to design and build scalable infrastructure for AI/ML and LLM workloads from scratch. You will own end-to-end deployment pipelines, GPU-enabled infrastructure, and production model serving.

The role requires deep hands-on experience with Kubernetes, Docker, Terraform/Ansible/Pulumi, and cloud/Linux environments to optimize performance, cost, and reliability while collaborating with AI/ML engineers.

Qualifications

• 6+ years of MLOps or related infra experience.
• Hands-on with GPU provisioning for AI workloads.
• Production deployment of ML models and LLMs.
• Strong Kubernetes and container orchestration knowledge.
• Experience with IaC tools: Terraform, Ansible, Pulumi.

Responsibilities

• Design end-to-end MLOps infrastructure for training, deployment, monitoring, and inference.
• Build CI/CD pipelines for ML models and automate deployment, validation, and rollback.
• Provision, configure, and optimize GPU-enabled infrastructure for AI/ML workloads.
• Deploy and scale LLM inference and model-serving solutions in production.
• Manage Kubernetes, Docker, infrastructure-as-code, and distributed systems.
• Implement monitoring, logging, observability, and automated remediation for production workloads.
• Optimize GPU/CPU utilization, latency, performance, and costs.
• Collaborate with AI/ML engineers to build scalable AI platforms.

Skills

MLOps experience

GPU provisioning

Kubernetes

Docker

Infrastructure-as-code

Python or Go

ML CI/CD / model serving

Cloud infrastructure

Tools

Terraform

Ansible

Pulumi

Job description

Trine Infotech is seeking an experienced MLOps Engineer to design and build scalable infrastructure for AI/ML and LLM workloads from scratch. You will own end-to-end deployment pipelines, GPU-enabled infrastructure, and production model serving.

The role requires deep hands-on experience with Kubernetes, Docker, Terraform/Ansible/Pulumi, and cloud/Linux environments to optimize performance, cost, and reliability while collaborating with AI/ML engineers.