AI Platform Engineer/DevOp
About The Position
The AI Platform Engineer builds, operates, and scales infrastructure and tooling for AI/ML and generative AI workloads. The role focuses on cloud platform engineering, CI/CD automation, and MLOps practices that support reliable, secure, and scalable delivery of AI systems. This is an onsite position in Charlotte, North Carolina. The engineer partners with AI/ML engineers and data scientists to productionize models and improve the path from experimentation to production. They also help ensure platform security and compliance, automate operational workflows, and resolve production infrastructure issues.
Requirements
- At least 7 years of experience in platform engineering, DevOps, or infrastructure engineering roles.
- Strong hands-on expertise with GCP, including Vertex AI, GKE, Cloud Run, IAM, VPC/networking, and BigQuery.
- Deep experience designing and managing CI/CD pipelines, using tools such as Cloud Build, Jenkins, GitHub Actions, GitLab CI, or ArgoCD.
- Proficiency in Infrastructure as Code; Terraform is preferred.
- Strong scripting or programming skills in Python, Bash, or Go.
- Experience with Docker and Kubernetes.
- Solid understanding of MLOps concepts and the ML lifecycle, including training, versioning, deployment, and monitoring.
- Experience with observability tools such as Cloud Monitoring, Prometheus, Grafana, or ELK/EFK.
- Strong understanding of cloud security, IAM, and secrets management best practices.
Nice To Haves
- GCP Professional certification, such as Cloud DevOps Engineer, Cloud Architect, or Machine Learning Engineer.
- Experience supporting generative AI or LLM platforms, such as Vertex AI, Gemini Enterprise, or Model Garden.
- Experience with GitOps workflows such as ArgoCD or Flux.
- Background working in regulated or enterprise-scale environments.
- Familiarity with GCP cost optimization and FinOps practices.
Responsibilities
- Design, build, and maintain scalable GCP cloud infrastructure for 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 (GKE).
- Establish observability, logging, and alerting for AI/ML services.
- Partner with AI/ML engineers and data scientists to productionize models and streamline the transition from experimentation to production.
- Ensure platform security, IAM policies, secrets management, and compliance across environments.
- Automate build, test, deployment, and rollback processes to reduce manual toil.
- Troubleshoot platform and infrastructure issues and drive root-cause resolution for production incidents.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
