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 #J-18808-Ljbffr Capgemini