Cloud Engineer - OpenShift & AI Platform Engineering

Role: Cloud Engineer - OpenShift & AI Platform Engineering

Locations: Charlotte, NC/ Edison, NJ/ Iselin, NJ/ Irving, TX (Hybrid Onsite)

Duration: 12+ Months Contract

F2F Interview MUST

Note: Candidate needs to be in the office 3-4 Days every week. Local or candidates from adjacent states only.

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 Google Cloud Platform.
• 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