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.

• 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, Git Hub Actions, Jenkins, Git Lab 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 product ionize 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

• 7+ years of experience in platform engineering, Dev Ops, or infrastructure engineering
• Hands-on expertise in Google Cloud Platform (GCP), including Vertex AI, GKE, Cloud Run, IAM, VPC/networking, and Big Query
• Deep experience designing and managing CI/CD pipelines, including options such as Cloud Build, Jenkins, Git Hub Actions, Git Lab 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

GCP
, Vertex AI, GKE, Cloud Run, IAM, VPC/networking, Big Query, Cloud Build, Git Hub Actions, Jenkins, Git Lab CI, Vertex AI Pipelines, Kubeflow, MLflow, Terraform, Deployment Manager, Docker, Kubernetes, Prometheus, Grafana, Cloud Monitoring, Cloud Logging, Python, Bash, Go, ELK/EFK, ArgoCD.

• Medical, dental, vision, and retirement benefits