AI Platform Engineer

AI Platform Engineer

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

Job Overview

We are seeking aSenior AI Platform Engineer to build, operate, and scale the infrastructure and tooling that powerAI/ML and Generative AI workloads. The role focuses oncloud platform engineering, CI/CD automation, MLOps, security, observability, and reliable production delivery of AI systems.

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 release workflows.
• Build and maintainMLOps pipelines for model versioning, training, deployment, and monitoring.
• ImplementInfrastructure as Code (IaC) using Terraform and Deployment Manager.
• Containerize and orchestrate services usingDocker and Kubernetes/GKE.
• Establish observability, logging, and alerting for AI/ML services usingCloud Monitoring, Cloud Logging, Prometheus, and Grafana.
• Partner with AI/ML engineers and data scientists to productionize models and streamline the path from experimentation to production.
• Ensure platform security throughIAM policies, secrets management, and compliance controls.
• Drive automation across build, test, deployment, and rollback processes.
• Troubleshoot platform and infrastructure issues and performroot-cause analysis for production incidents.

Required Qualifications

• 7+ years of experience in Platform Engineering, DevOps, Infrastructure Engineering, or related roles.
• Strong hands-on expertise withGoogle Cloud Platform (GCP), including:
  • Vertex AI
  • GKE
  • Cloud Run
  • IAM
  • VPC / Networking
  • BigQuery
• Deep experience designing and managingCI/CD pipelines using:
  • Cloud Build
  • Jenkins
  • GitHub Actions
  • GitLab CI
  • ArgoCD
• Strong proficiency inInfrastructure as Code, preferably Terraform.
• Strong scripting/programming skills usingPython, Bash, or Go.
• Experience withDocker and Kubernetes.
• Strong understanding ofMLOps and the ML lifecycle, including:
  • Training
  • Versioning
  • Deployment
  • Monitoring
• Experience with observability tools such as:
  • Cloud Monitoring
  • Prometheus
  • Grafana
  • ELK / EFK
• Strong understanding ofcloud security, IAM, and secrets management best practices.

Preferred Qualifications

• GCP Professional certifications such as:
  • Professional Cloud DevOps Engineer
  • Professional Cloud Architect
  • Professional Machine Learning Engineer
• Experience supportingGenerative AI / LLM platforms, including:
  • Vertex AI
  • Gemini Enterprise
  • Model Garden
• Experience withGitOps workflows using ArgoCD or Flux.
• Experience working inregulated or enterprise-scale environments.
• Familiarity withGCP cost optimization and FinOps practices.

Core Skills

GCP | Vertex AI | GKE | Cloud Run | BigQuery | IAM | VPC/Networking | Terraform | Docker | Kubernetes | CI/CD | Cloud Build | Jenkins | GitHub Actions | GitLab CI | ArgoCD | MLOps | Kubeflow | MLflow | Python | Bash | Go | Prometheus | Grafana | ELK/EFK | Cloud Monitoring | Cloud Logging | Generative AI | LLM | Gemini Enterprise | Model Garden | GitOps | Cloud Security | Secrets Management | FinOps