Senior ML Platform Architect & SRE Lead

NTT DATA North America; Charlotte (NC); On-site; USD 84,000 - 125,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with company match

Paid time off

Job summary

NTT DATA North America is seeking a Lead ML Platform Engineer to set the architecture and lead hands-on development of the Cortex Predictive AI Platform across cloud and on‑premises in Charlotte, NC. You will drive secure, scalable standards enabling data scientists and ML engineers to build, deploy, monitor, and operate models with reliability.

The role requires leadership in platform lifecycle design, governance, and CI/CD, with emphasis on production-grade ML infrastructure.

Qualifications

• 8+ years in platform engineering, SRE, MLOps, or related roles.
• 4+ years designing or operating enterprise AI/ML platforms.
• Experience leading architecture for complex cloud/on-prem platforms.
• Strong hands-on GCP and multi/cloud knowledge.
• Kubernetes-based platforms (GKE/OpenShift) in production.

Responsibilities

• Define target architecture for predictive AI and ML platform across cloud/on-prem.
• Build and operate reusable platform services for end-to-end ML lifecycle.
• Lead platform engineering for multi-cloud environments with secure connectivity.
• Design and operate Kubernetes-based ML platforms (GKE/OpenShift).
• Implement MLOps capabilities for experiment tracking and model deployment.
• Create CI/CD pipelines and infrastructure automation for platform services.
• Enable real-time and batch inference with scalable patterns.
• Mentor engineers and document patterns for self-service adoption.

Skills

GCP

Hybrid cloud

Kubernetes

MLOps

Python

CI/CD

Tools

GKE

OpenShift

Terraform

Helm

Argo CD

Jenkins

GitHub Actions

GitLab CI

Job description

NTT DATA North America is seeking a Lead ML Platform Engineer to set the architecture and lead hands-on development of the Cortex Predictive AI Platform across cloud and on‑premises in Charlotte, NC. You will drive secure, scalable standards enabling data scientists and ML engineers to build, deploy, monitor, and operate models with reliability.

The role requires leadership in platform lifecycle design, governance, and CI/CD, with emphasis on production-grade ML infrastructure.