Senior MLOps Platform Engineer: Cloud Infra & Pipelines

RaceDog Technologies; Charlotte (NC); On-site; USD 150,000 - 210,000; Full time

Job summary

RaceDog Technologies in Charlotte, NC is seeking an exceptional MLOps / Platform Engineer for a full-time onsite role. You will design secure cloud networking, IAM governance, and scalable ML deployment pipelines, partnering with data scientists and security teams.

The ideal candidate has 12+ years in platform engineering and DevOps, deep AWS/Azure expertise, and hands-on Terraform, Kubernetes, Docker, and MLflow/Kubeflow experience.

Qualifications

• Bachelor’s degree in Computer Science, IT, Software Engineering, or related field.
• 12+ years in IT with 6+ years in DevOps, cloud platform engineering and MLOps.
• Deep proficiency in cloud networking (VPC/VNet), IAM governance, load balancers and API gateways.
• Hands-on experience with MLOps pipelines (MLflow, Kubeflow, SageMaker or Vertex AI).
• Experience with Terraform and container orchestration (Kubernetes/Docker).
• Onsite residency in Charlotte, NC.

Responsibilities

• Architect and maintain scalable MLOps platforms and cloud infrastructure.
• Configure secure networking, IAM policies, and API gateways.
• Automate ML model CI/CD pipelines and deployment processes.
• Collaborate with data scientists and security teams on production readiness.
• Mentor junior engineers and document architecture runbooks.

Skills

VPC/VNet networking

IAM governance

Load balancers

API gateways

MLOps pipelines

Terraform

Kubernetes

Docker

Education

Bachelor’s degree in Computer Science, IT, or related

Job description

RaceDog Technologies in Charlotte, NC is seeking an exceptional MLOps / Platform Engineer for a full-time onsite role. You will design secure cloud networking, IAM governance, and scalable ML deployment pipelines, partnering with data scientists and security teams.

The ideal candidate has 12+ years in platform engineering and DevOps, deep AWS/Azure expertise, and hands-on Terraform, Kubernetes, Docker, and MLflow/Kubeflow experience.