GPU MLOps Engineer

Job not on LinkedIn

🕒 August 22

🏄 California, Illinois, +1 more states – Remote

🏄 California – Remote 🌽 Illinois – Remote 🤠 Texas – Remote

💵 $200k - $280k / year

⏰ Full Time

🟠 Senior

🔴 Lead

🤖 Machine Learning Engineer

👻 Ghost score 3%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Guardian Industries - DeWitt

WebsiteLinkedInAll Job Openings

- employees

🏭 Manufacturing

🏗️ Construction

Manufacturing • Construction

Guardian Industries - DeWitt is a facility or operating unit of Guardian Industries listed among Koch companies. Guardian Industries is a global manufacturer best known for producing glass and building-related products and operates multiple manufacturing sites; the DeWitt location is presented in Koch’s careers/company listings as one of the Guardian Industries locations offering roles in manufacturing, engineering, operations and related functions. As part of Koch’s portfolio, the site is positioned within a large industrial/manufacturing network and supports production, operations and career opportunities tied to Guardian’s glass and building products businesses.

📋 Description

• Own the Azure platform layer end-to-end for the engineering AI/ML platform • Build and maintain Azure DevOps CI/CD pipelines for ML model training, validation, versioning, and deployment • Provision and scale Azure GPU compute (ND/NC series) and AKS/ACI container infrastructure for training and simulation workloads • Automate model retraining and redeployment workflows in Azure ML pipelines as new data becomes available • Implement access control, identity management, and secrets management using Microsoft Entra ID and Azure Key Vault • Monitor and optimize Azure GPU/cloud spend using Azure Cost Management • Build cost visibility dashboards in Power BI • Partner with data scientists, ML engineers, and LLM engineers to maintain platform operations

🎯 Requirements

• 10+ years in MLOps, DevOps, or Cloud Infrastructure, with exposure to ML deployment and cloud infrastructure management • Strong Azure experience, including GPU compute (ND/NC), Azure Kubernetes Service, and Docker containerization • Infrastructure-as-code experience with Terraform or Bicep • Solid grasp of cloud security fundamentals, including IAM, network security, and secrets management • Demonstrated cloud cost optimization experience, including right-sizing, autoscaling, and spot/preemptible strategies • Hands-on experience with model registries and experiment tracking using Azure ML and MLflow • GPU-heavy workload experience, such as ML training or HPC simulation, preferred • Azure certifications, including Solutions Architect Expert or Security Engineer Associate, preferred • Experience with HPC job schedulers, such as Slurm via Azure CycleCloud, preferred • Experience supporting engineering simulation tools, preferred

🏖️ Benefits

• Variable pay, issued as a monetary bonus or in another form • Medical insurance • Dental insurance • Vision insurance • Flexible spending accounts • Health savings accounts • Life insurance • ADD • Disability benefits • Retirement benefits • Paid vacation/time off • Educational assistance • Infertility assistance may be included • Paid parental leave may be included • Adoption assistance may be included • Flexible work environment