AI Platform Engineer – Agentic AI

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

Job Overview

We are seeking a Senior AI Platform Engineer to build, operate, and scale the infrastructure and tooling that power AI/ML and Generative AI workloads. The role focuses on cloud platform engineering, CI/CD automation, MLOps, security, observability, and production AI infrastructure.

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 release workflows.
• Build and maintain MLOps pipelines for model versioning, training, deployment, and monitoring.
• Implement Infrastructure as Code (IaC) using Terraform and Deployment Manager.
• Containerize and orchestrate services using Docker and Kubernetes/GKE.
• Establish observability, logging, and alerting for AI/ML services using Cloud Monitoring, Cloud Logging, Prometheus, and Grafana.
• Partner with AI/ML engineers and data scientists to productionize models and streamline the transition from experimentation to production.
• Implement platform security, IAM policies, secrets management, and compliance across environments.
• Automate build, testing, deployment, and rollback processes to reduce manual effort.
• Troubleshoot platform and infrastructure issues and perform root-cause analysis for production incidents.

Required Qualifications

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

Preferred Qualifications

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

Good to Have

• BigQuery
• Cloud Storage
• Pub/Sub
• Terraform
• Cloud Build
• Kubeflow
• SQL
• APIs
• Model Monitoring
• RAG
• Gemini / Generative AI

Core Skills

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