AI Platform Engineer

Position Title

AI Platform Engineer

Location

Charlotte, NC, Onsite

Duration

Long Term Contract

Job description

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. This role focuses on cloud platform engineering, CI/CD automation, and MLOps practices that enable reliable, secure, and scalable delivery of AI systems.

Basic Qualifications

7 years of experience in platform engineering, DevOps, or infrastructure engineering roles

Required Skills

• Strong hands-on expertise with Google Cloud Platform (GCP) — Vertex AI, GKE, Cloud Run, IAM, VPC/networking, BigQuery
• Deep experience designing and managing CI/CD pipelines (Cloud Build, Jenkins, GitHub Actions, GitLab CI, ArgoCD)
• Proficiency in Infrastructure as Code (Terraform preferred)
• Strong scripting/programming skills (Python, Bash, or Go)
• Experience with containerization and orchestration (Docker, Kubernetes)
• Solid understanding of MLOps concepts and the ML lifecycle (training, versioning, deployment, monitoring)
• Experience with observability tooling (Cloud Monitoring, Prometheus, Grafana, ELK/EFK)
• Strong understanding of cloud security, IAM, and secrets management best practices

Preferred Qualifications

• GCP Professional certifications (Cloud DevOps Engineer, Cloud Architect, or Machine Learning Engineer)
• Experience supporting generative AI / LLM platforms (Vertex AI, Gemini Enterprise, Model Garden)
• Experience with GitOps workflows (ArgoCD, Flux)
• Background working in regulated or enterprise-scale environments
• Familiarity with cost optimization and FinOps practices on GCP

Role and responsibilities

• Design, build, and maintain scalable cloud infrastructure on GCP to support 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 for repeatable, auditable environment provisioning
• Containerize and orchestrate services using Docker and Kubernetes
• Establish observability, logging, and alerting for AI/ML services
• Partner with AI/ML engineers and data scientists to productionize models and streamline the path from experimentation to production
• Ensure platform security, IAM policies, secrets management, and compliance across environments
• Drive automation to reduce manual toil across build, test, deployment, and rollback processes
• Troubleshoot platform/infrastructure issues and drive root-cause resolution for production incidents