AI Engineer

Build and scale AI platform infrastructure

for AI/ML and generative AI workloads on Google Cloud at

Capgemini . This

contract

role is based in

Charlotte, NC (onsite)

and focuses on cloud platform engineering, CI/CD automation, and MLOps practices to support reliable, secure, and scalable delivery. If you enjoy turning experimentation into production-ready systems, you will work closely with AI/ML engineers and data scientists to streamline the full path from training to monitoring. What you’ll do

Design, build, and maintain

scalable GCP cloud infrastructure

for AI/ML and application workloads Architect and manage

CI/CD pipelines

for model training, deployment, and application release workflows (Cloud Build, GitHub Actions, Jenkins, GitLab CI, or similar) Create and run

MLOps pipelines

for model versioning, training, deployment, and monitoring using tools such as

Vertex AI Pipelines ,

Kubeflow , and

MLflow Use

Infrastructure as Code

(Terraform, Deployment Manager) for repeatable and

auditable

environment provisioning Containerize and orchestrate services with

Docker

and

Kubernetes

(GKE) Set up

observability

including logging and alerting for AI/ML services using Cloud Monitoring, Cloud Logging, and Prometheus/Grafana Partner with AI/ML engineers and data scientists to productionize models and reduce friction between experimentation and deployment Implement platform security with

IAM , secrets management, and compliance across environments Drive automation to reduce manual toil across build, test, deployment, and rollback workflows Troubleshoot platform and infrastructure issues and lead root-cause resolution for production incidents What you bring

7+ years

of experience in platform engineering, DevOps, or infrastructure engineering Hands-on expertise in

Google Cloud Platform (GCP) , including Vertex AI, GKE, Cloud Run, IAM, VPC/networking, and BigQuery Deep experience designing and managing CI/CD pipelines, including options such as Cloud Build, Jenkins, GitHub Actions, GitLab CI, and ArgoCD Proficiency with

Infrastructure as Code

(Terraform preferred) Strong scripting and programming skills in

Python ,

Bash , or

Go Experience with containerization and orchestration (Docker, Kubernetes) Working knowledge of

MLOps concepts

across the ML lifecycle: training, versioning, deployment, and monitoring Experience with observability tooling such as

Cloud Monitoring ,

Prometheus ,

Grafana , and ELK/EFK Strong understanding of cloud security, IAM, and secrets management best practices Tech stack you may work with

GCP , Vertex AI, GKE, Cloud Run, IAM, VPC/networking, BigQuery, Cloud Build, GitHub Actions, Jenkins, GitLab CI, Vertex AI Pipelines, Kubeflow, MLflow, Terraform, Deployment Manager, Docker, Kubernetes, Prometheus, Grafana, Cloud Monitoring, Cloud Logging, Python, Bash, Go, ELK/EFK, ArgoCD. Benefits

Medical, dental, vision, and retirement benefits

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