AI Engineer

Position Title: AI Engineer
Location: Charlotte, NC (Onsite)
Duration: Long Term Contract
 
Job Description:
We are seeking a AI Engineer to join our team. This role is crucial for building, operating, and scaling the infrastructure and tooling that power AI/ML and generative AI workloads. The successful candidate will focus on cloud platform engineering, CI/CD automation, and MLOps practices to enable reliable, secure, and scalable delivery of AI systems.
 
Basic Qualifications:

• 7 years of experience in platform engineering, DevOps, or infrastructure engineering roles
• Strong hands-on expertise with Google Cloud Platform (GCP)
• Proficiency in Infrastructure as Code (Terraform preferred)
• Strong scripting/programming skills (Python, Bash, or Go)
• Experience with containerization and orchestration (Docker, Kubernetes)

• Designing and managing CI/CD pipelines (Cloud Build, Jenkins, GitHub Actions, GitLab CI, ArgoCD)
• Solid understanding of MLOps concepts and the ML lifecycle
• Experience with observability tooling (Cloud Monitoring, Prometheus, Grafana, ELK/EFK)
• Strong understanding of cloud security, IAM, and secrets management best practices

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

• 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
• Ensure platform security, IAM policies, secrets management, and compliance
• 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