AI Platform Engineer

Location: Charlotte, NC – Onsite
Experience: 7+ Years

About the Role

We are seeking a senior AI Platform Engineer to build, operate, and scale the infrastructure and tooling that support AI/ML and Generative AI workloads. This role will focus on cloud platform engineering, CI/CD automation, MLOps, infrastructure automation, security, and observability to enable reliable and scalable delivery of AI solutions.

Key Responsibilities

• Design, build, and maintain scalable GCP cloud infrastructure supporting AI/ML and application workloads.
• Architect and manage CI/CD pipelines for model training, deployment, and application releases using Cloud Build, GitHub Actions, Jenkins, GitLab CI, or similar tools.
• Build and maintain MLOps pipelines for model versioning, training, deployment, and monitoring using Vertex AI Pipelines, Kubeflow, and MLflow.
• Implement Infrastructure as Code (IaC) using Terraform or similar tools for consistent and auditable environment provisioning.
• Containerize and orchestrate applications and AI services using Docker and Kubernetes/GKE.
• Establish monitoring, logging, alerting, and observability for AI/ML services using Cloud Monitoring, Cloud Logging, Prometheus, and Grafana.
• Partner with AI/ML engineers and data scientists to productionize models and improve the transition from experimentation to production.
• Implement and maintain cloud security, IAM, secrets management, and compliance across environments.
• Automate build, testing, deployment, rollback, and operational processes to reduce manual effort.
• Troubleshoot infrastructure and platform issues and lead root-cause analysis and resolution for production incidents.

Required Qualifications

• 7+ years of experience in Platform Engineering, DevOps, Cloud Infrastructure, or related roles.
• Strong hands-on experience with Google Cloud Platform (GCP), including Vertex AI, GKE, Cloud Run, IAM, VPC/networking, and BigQuery.
• Strong experience designing and managing CI/CD pipelines using Cloud Build, Jenkins, GitHub Actions, GitLab CI, ArgoCD, or similar technologies.
• Strong proficiency in Terraform and Infrastructure as Code practices.
• Strong scripting or programming skills using Python, Bash, or Go.
• Hands-on experience with Docker and Kubernetes.
• Solid understanding of MLOps and the machine learning lifecycle, including training, model versioning, deployment, and monitoring.
• Experience with observability and monitoring tools such as Cloud Monitoring, Prometheus, Grafana, and ELK/EFK.
• Strong understanding of cloud security, IAM, secrets management, and enterprise security best practices.

Preferred Qualifications

• GCP Professional certification such as Professional Cloud DevOps Engineer, Professional Cloud Architect, or Professional Machine Learning Engineer.
• Experience supporting Generative AI and LLM platforms, including Vertex AI, Gemini Enterprise, and Model Garden.
• Experience implementing GitOps workflows using ArgoCD or Flux.
• Experience working in regulated, highly secure, or enterprise-scale environments.
• Knowledge of GCP cost optimization and FinOps practices