Senior MLOps Engineer: Scalable AI Infra & On-Prem

4Minds; Dallas (TX); On-site; USD 130,000 - 200,000; Full time

Benefits offered by this job

Stock options

401(k) with company match

Unlimited PTO

11 paid holidays

Medical, dental, and vision coverage

Job summary

4Minds is seeking a senior ML Ops Engineer to own and scale our AI infrastructure across GCP, AWS, Azure, CoreWeave, and on‑premise deployments. You will design, build, and optimize the inference stack, CI/CD pipelines, and GPU performance to deliver reliable models at enterprise scale.

You will work closely with the CTO to push the boundaries of private, cloud-agnostic AI deployment, ensuring production-grade systems that can operate across diverse environments.

Qualifications

• 5+ years of hands-on production ML infrastructure experience.
• Bachelor's degree in Computer Science, Engineering, or related field.
• Deep proficiency with Kubernetes and Docker for deploying AI workloads.
• Hands-on experience with CI/CD pipelines for ML model lifecycle management.
• Strong working knowledge of Nvidia Triton Inference Server and TensorRT.
• Experience designing and managing infrastructure across multiple cloud platforms (GCP/AWS/Azure/CoreWeave).
• Solid understanding of GPU cluster management and hardware tradeoffs.
• Experience with on-premise AI deployment and related infra complexity.
• Strong MLOps principles and AI model lifecycle management from experimentation to production.
• Ability to work autonomously and communicate technical tradeoffs clearly to leadership.

Responsibilities

• Design and maintain CI/CD pipelines for AI models from dev to production.
• Own inference pipeline reliability and performance across cloud and on‑prem environments.
• Research GPU scaling approaches to inform infrastructure decisions and extend capabilities.
• Implement and manage Nvidia Triton Inference Server and Fleet Command workflows.
• Deploy models using Kubernetes and Docker for scalable serving.
• Automate model retraining and redeployment in response to data changes.
• Monitor system health with AI observability tools and pursue continuous improvement.
• Collaborate with CTO on infrastructure research initiatives and production readiness.
• Support early on-premise installations and knowledge transfer to Solutions Engineering.

Skills

Autonomy

Communication

Problem solving

Education

Bachelor's degree in CS/Engineering

Tools

Kubernetes

Docker

Nvidia Triton Inference Server

TensorRT

CI/CD for ML

Nvidia Fleet Command

Job description

4Minds is seeking a senior ML Ops Engineer to own and scale our AI infrastructure across GCP, AWS, Azure, CoreWeave, and on‑premise deployments. You will design, build, and optimize the inference stack, CI/CD pipelines, and GPU performance to deliver reliable models at enterprise scale.

You will work closely with the CTO to push the boundaries of private, cloud-agnostic AI deployment, ensuring production-grade systems that can operate across diverse environments.