AI Platform Engineer / DevOps | Ampcus | Charlotte, NC

Position Summary: Ampcus is actively seeking an experienced, high-caliber, and technical AI Platform Engineer / DevOps Specialist to join our enterprise infrastructure team in Charlotte, NC on a hybrid 6+ month contract basis. In this critical senior platform engineering role, you will be responsible for building, scaling, and maintaining advanced cloud infrastructure, MLOps platforms, and CI/CD pipelines supporting cutting‑edge generative AI and LLM workloads on Google Cloud Platform (GCP). You will leverage your deep practical expertise in Vertex AI, GKE, Kubernetes, Terraform, and cloud automation to ensure uncompromised performance and security. This opportunity offers competitive compensation, executive project visibility, and excellent professional growth prospects within a leading global technology and staffing consultancy (Note: Open exclusively to candidates currently based locally in Charlotte, NC for hybrid work). Detailed Job Description: As an AI Platform Engineer / DevOps Specialist supporting cloud infrastructure and MLOps platforms in Charlotte, NC via Ampcus, you will take full operational, tactical, and technical ownership of cloud architecture, pipeline automation, and LLM deployment pipelines. Your day-to-day responsibilities include designing and managing enterprise CI/CD pipelines (Cloud Build, Jenkins, GitHub Actions, GitLab CI, ArgoCD), building scalable infrastructure using Terraform, and writing robust automation scripts in Python, Bash, or Go. You will manage containerization and orchestration via Docker and Kubernetes, optimize Google Cloud Platform (GCP) resources including Vertex AI, GKE, Cloud Run, IAM, VPC networking, and BigQuery, and enforce rigorous cloud security and secrets management best practices. Furthermore, you will establish comprehensive observability monitoring (Cloud Monitoring, Prometheus, Grafana, ELK/EFK) and support end-to-end MLOps lifecycles from model training and versioning to production deployment. Key Responsibilities: Design, build, scale, and maintain robust cloud infrastructure, MLOps platforms, and DevOps pipelines on Google Cloud Platform (GCP). Deploy, manage, and optimize enterprise AI platforms leveraging GCP Vertex AI, GKE, Cloud Run, IAM, VPC networking, and BigQuery. Design, implement, and maintain advanced CI/CD pipelines using Cloud Build, Jenkins, GitHub Actions, GitLab CI, and ArgoCD. Build and maintain Infrastructure as Code (IaC) modules and configurations using Terraform. Write robust automation scripts and tooling using Python, Bash, or Go. Manage containerization and container orchestration clusters using Docker and Kubernetes. Oversee end-to-end MLOps lifecycles including model training, artifact versioning, automated deployment, and production monitoring. Implement comprehensive observability and monitoring solutions utilizing Cloud Monitoring, Prometheus, Grafana, and ELK/EFK stacks. Enforce strict cloud security standards, IAM role-based access control, and secrets management best practices. Collaborate closely with data scientists, machine learning engineers, and software development squads to deliver scalable AI solutions. Required Qualifications & Skills: Bachelor’s degree in Computer Science, Information Technology, Software Engineering, or a related technical discipline. Minimum of 7+ years of professional IT experience in platform engineering, DevOps, or infrastructure engineering roles. Strong, proven hands‑on expertise with Google Cloud Platform (GCP) core services including Vertex AI, GKE, Cloud Run, IAM, VPC, and BigQuery. Deep practical experience designing and managing CI/CD pipelines across Cloud Build, Jenkins, GitHub Actions, GitLab CI, and ArgoCD. Advanced proficiency in Infrastructure as Code (IaC) utilizing Terraform. Strong scripting and software development skills in Python, Bash, or Go. Comprehensive experience with containerization and orchestration platforms (Docker, Kubernetes). Solid understanding of MLOps concepts, machine learning lifecycles, model training, and deployment workflows. Familiarity with observability tooling including Cloud Monitoring, Prometheus, Grafana, and ELK/EFK. Mandatory local residency and physical presence in Charlotte, NC for hybrid work arrangements (strictly open to local candidates). Nice-to-Have Skills: Active GCP Professional certifications (e.g., Google Cloud Certified Professional Cloud DevOps Engineer, Cloud Architect, or Machine Learning Engineer). Direct hands‑on experience supporting generative AI / LLM platforms including Vertex AI, Gemini Enterprise, and Model Garden. Practical experience implementing GitOps workflows using ArgoCD or Flux. Background working in highly regulated financial services or enterprise-scale environments. Familiarity with cloud cost optimization, financial operations (FinOps), and resource budgeting on GCP. Salary/Rate: Market Competitive / Enterprise Hybrid Contract Rate Deadline: Open until filled (Urgent Hiring) Notice Period: Immediate to 2 Weeks Contract Duration: 6+ Months (Hybrid Contract, Charlotte, NC) #J-18808-Ljbffr Tech Junction Ltd