AI Platform Engineer

AI Platform Engineer

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

About the Role

We are seeking a seniorAI Platform Engineer to build, operate, and scale the infrastructure and tooling that supportAI/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 scalableGCP cloud infrastructure supporting AI/ML and application workloads.
• Architect and manageCI/CD pipelines for model training, deployment, and application releases using Cloud Build, GitHub Actions, Jenkins, GitLab CI, or similar tools.
• Build and maintainMLOps pipelines for model versioning, training, deployment, and monitoring using Vertex AI Pipelines, Kubeflow, and MLflow.
• ImplementInfrastructure as Code (IaC) using Terraform or similar tools for consistent and auditable environment provisioning.
• Containerize and orchestrate applications and AI services usingDocker and Kubernetes/GKE.
• Establish monitoring, logging, alerting, and observability for AI/ML services usingCloud 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 maintaincloud 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 leadroot-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 withGoogle Cloud Platform (GCP), including Vertex AI, GKE, Cloud Run, IAM, VPC/networking, and BigQuery.
• Strong experience designing and managingCI/CD pipelines using Cloud Build, Jenkins, GitHub Actions, GitLab CI, ArgoCD, or similar technologies.
• Strong proficiency inTerraform and Infrastructure as Code practices.
• Strong scripting or programming skills usingPython, Bash, or Go.
• Hands-on experience withDocker and Kubernetes.
• Solid understanding ofMLOps and the machine learning lifecycle, including training, model versioning, deployment, and monitoring.
• Experience withobservability and monitoring tools such as Cloud Monitoring, Prometheus, Grafana, and ELK/EFK.
• Strong understanding ofcloud security, IAM, secrets management, and enterprise security best practices.

Preferred Qualifications

• GCP Professional certification such asProfessional Cloud DevOps Engineer, Professional Cloud Architect, or Professional Machine Learning Engineer.
• Experience supportingGenerative AI and LLM platforms, including Vertex AI, Gemini Enterprise, and Model Garden.
• Experience implementingGitOps workflows using ArgoCD or Flux.
• Experience working inregulated, highly secure, or enterprise-scale environments.
• Knowledge ofGCP cost optimization and FinOps practices