Cloud AI Engineer

Cloud AI Engineer

Location: Charlotte , NC (Onsite)

Job Type: Contract

Experience

6 to 12 Years

Job Summary:

• We are seeking a highly skilled Cloud Engineer with deep expertise in Red Hat OpenShift, Kubernetes, cloud-native technologies, and modern platform engineering practices. The ideal candidate will be responsible for designing, building, automating, and supporting enterprise OpenShift platforms while enabling AI/ML and Generative AI workloads across hybrid cloud environments.
• This role requires strong hands-on experience in container orchestration, infrastructure automation, DevSecOps, cloud services, and AI platform enablement. The candidate will work closely with application teams, data scientists, MLOps engineers, and enterprise architects to deliver scalable, secure, and resilient cloud platforms.

Key Responsibilities:

• OpenShift Platform Engineering
• Design, implement, and manage enterprise-grade OpenShift clusters across on-premises and cloud environments.
• Administer OpenShift Container Platform (OCP), including installation, upgrades, patching, and lifecycle management.
• Configure and manage Operators, Routes, Ingress Controllers, Storage Classes, and Networking components.
• Monitor cluster health, capacity, performance, and availability.
• Troubleshoot platform, networking, storage, and container-related issues.

Cloud & Infrastructure

• Build and manage cloud infrastructure on AWS, Azure, or GCP.
• Implement Infrastructure as Code (IaC) using Terraform, Ansible, and GitOps frameworks.
• Automate infrastructure provisioning and configuration management.
• Design highly available and scalable cloud-native architectures.

AI & GenAI Platform Enablement

• Support deployment and operationalization of AI/ML workloads on OpenShift.
• Enable AI platforms such as:
• OpenShift AI (formerly Red Hat OpenShift Data Science)
• NVIDIA AI Enterprise
• Kubeflow
• MLflow
• Ray Clusters
• Deploy and manage Large Language Models (LLMs) in enterprise environments.
• Support vector databases, RAG architectures, and AI application deployment platforms.
• Collaborate with Data Scientists and AI Engineers to operationalize ML models.

DevOps & MLOps

• Implement and maintain CI/CD pipelines using Jenkins, GitHub Actions, GitLab, or Azure DevOps.
• Develop GitOps solutions using ArgoCD or FluxCD.
• Enable MLOps workflows for model deployment, versioning, monitoring, and governance.
• Automate operational tasks using Python, Bash, or Go.

Security & Governance

• Implement OpenShift security best practices.
• Configure RBAC, IAM integration, network policies, secrets management, and certificate management.
• Ensure compliance with enterprise security and regulatory standards.
• Perform vulnerability remediation and container security scanning using tools such as Aqua, Prisma Cloud, Trivy, or Snyk.

Monitoring & Reliability:

Build observability solutions using:

Prometheus

Grafana

ELK/OpenSearch

Splunk

Dynatrace

Proactively monitor platform performance and reliability.

Support SRE practices, incident management, and root-cause analysis.

Required Skills

Core OpenShift Skills

Red Hat OpenShift Container Platform (OCP)

Kubernetes Administration

Container Technologies (Docker, Podman, CRI-O)

OpenShift Operators

OpenShift Virtualization

Service Mesh (Istio/Red Hat Service Mesh)

Cloud Technologies

AWS, Azure, or Google Cloud Platform

Cloud Networking

Storage and Compute Services

Hybrid Cloud Architecture

DevOps / Automation

Terraform

Ansible

GitHub Actions

Jenkins

GitLab CI/CD

ArgoCD

Programming

Python

Shell Scripting

Go (Preferred)

AI / GenAI Technologies

OpenShift AI

Kubeflow

MLflow

LangChain

Vector Databases (Pinecone, Chroma, FAISS, Milvus)

Retrieval-Augmented Generation (RAG)

LLM Deployment and Inference Platforms

NVIDIA GPU Workloads