Senior AI Platform Engineer: Cloud MLOps & CI/CD (Contract)

Capgemini; Charlotte (NC); On-site; USD 51,000 - 80,000; Full time

Job summary

Agentic AI in Charlotte, NC, is seeking a senior AI Platform Engineer to build, operate, and scale the infrastructure and tooling for AI/ML workloads. This role centers on cloud platform engineering, CI/CD automation, and MLOps practices to enable reliable, secure, and scalable AI delivery.

The candidate collaborates with AI/ML engineers and data scientists to productionize models, design scalable GCP infrastructure, and implement IaC, containers, and observability to drive production readiness

Qualifications

• 7+ years in platform, DevOps, or infra engineering.
• Hands-on with Google Cloud Platform (Vertex AI, GKE, Cloud Run, IAM, VPC).
• Expertise in CI/CD pipelines and IaC (Terraform preferred).
• Strong scripting in Python, Bash, or Go; containerization with Docker/Kubernetes.
• Experience with observability tooling and cloud security practices.

Responsibilities

• Design, build, and maintain scalable GCP-driven infra for AI workloads.
• Architect and run CI/CD pipelines for model training, deployment, and app release workflows.
• Develop and maintain MLOps pipelines for versioning, training, deployment, and monitoring.
• Implement Infrastructure as Code (Terraform, Deployment Manager) for repeatable provisioning; containerize services with Docker/Kubernetes.
• Establish observability and alerting using Cloud Monitoring, Prometheus, Grafana.
• Collaborate with AI/ML teams to productionize models and ensure security across environments.

Skills

GCP

CI/CD

MLOps

Python Bash Go

Docker Kubernetes

Security IAM

Tools

Terraform

Cloud Build

Jenkins

GitHub Actions

GitLab CI

ArgoCD

Docker

Kubernetes

Job description

Agentic AI in Charlotte, NC, is seeking a senior AI Platform Engineer to build, operate, and scale the infrastructure and tooling for AI/ML workloads. This role centers on cloud platform engineering, CI/CD automation, and MLOps practices to enable reliable, secure, and scalable AI delivery.

The candidate collaborates with AI/ML engineers and data scientists to productionize models, design scalable GCP infrastructure, and implement IaC, containers, and observability to drive production readiness