DevOps Deployment Engineer
Indexed description
About the Role:
Work directly with Tier-1 customers, deploy our platform in their Kubernetes and OpenShift environments, and solve whatever stands in the way, from infrastructure and networking to identity integrations.
Responsibilities:
- Deploy the Jeen.ai platform in customer Kubernetes and OpenShift environments, both on-prem (including air-gapped and GPU environments) and in the cloud.
- Troubleshoot and resolve deployment and runtime issues, working across the application, container, and infrastructure layers.
- Diagnose networking problems end to end: DNS, ingress and routes, load balancers, firewalls, proxies, TLS certificates, and network policies.
- Integrate the platform with customer identity providers (Active Directory, Azure AD/Entra ID, Okta, Keycloak) over SAML, OIDC, and LDAP.
- Own the full implementation lifecycle: installation, integration, customization, and post-deployment support.
- Work directly with IT, security, and infrastructure teams at banks, defense organizations, and other regulated enterprises.
- Feed field insights back to R&D and Product to improve our deployment process and documentation.
- 2 + years of hands-on experience in Professional Services, DevOps, Systems, or Integration roles.
- Deep, hands-on Kubernetes experience, including troubleshooting production environments.
- Hands-on experience with OpenShift
- Strong Helm skills, including customizing and troubleshooting charts.
- Strong Docker and container experience, including working with private registries.
- Solid networking knowledge: TCP/IP, DNS, TLS/certificates, proxies, load balancing, and firewalls.
- Hands-on experience integrating with IDPs using SAML, OIDC, or LDAP.
- Strong Linux skills and scripting (Bash or Python).
- Strong ownership, seeing problems through until they're solved.
- Clear communication with enterprise customers, including under pressure.
- High-level English, written and spoken.
- Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP), including managed Kubernetes services such as EKS, AKS, or GKE.
- Air-gapped or offline Kubernetes installations.
- NVIDIA GPU environments.
- Exposure to LLMs, RAG, or AI/ML workloads.
- Experience in highly regulated environments.
- Relevant certifications.
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