Job Context:
Riseup Labs is seeking a senior Deployment Engineer to enable reliable, secure, and repeatable deployment of the pod’s deliverables into enterprise environments. This role focuses on CI/CD, containerized deployments, controlled environment promotion, operational readiness, and production stability.The Deployment Engineer will work closely with engineering teams, architecture, and platform stakeholders to ensure delivery pipelines and runtime environments support rapid iteration without compromising security, governance, reliability, or cost controls.
Job Responsibilities:
Educational Requirements:
Additional Requirements:
Preferred / Nice to Have (Senior Differentiators) :
• Strong Kubernetes operational experience (deploy, scale, troubleshoot, upgrade).
• Experience implementing GitOps patterns (ArgoCD or equivalent).
• Familiarity with Azure-native deployment practices and access control models.
• Experience with infrastructure-as-code (Terraform, Bicep, or equivalent).
• Familiarity with observability tooling and SLO-driven operations (logs, metrics, traces, dashboards, alerts).
• Experience supporting secure networking patterns (private endpoints, restricted outbound connectivity, enterprise firewall constraints).
• Experience optimizing delivery workflows to reduce deployment risk while increasing release frequency.
Technology Stack (Indicative) :
(Final stack and tooling will follow internal standards and may vary by domain.)
• CI/CD pipelines and release management
• Docker
• Kubernetes (where applicable)
• GitOps tooling (ArgoCD or equivalent, where applicable)
• Cloud services (Azure-first where applicable)
• Centralized secrets management (Key Vault/Vault-style tooling)
• Infrastructure-as-code (where applicable)
• Monitoring and operational tooling (metrics, logs, traces)
Cross-Cutting Expectations :
• Strong ownership for deployment reliability and operational stability.
• Structured execution with a focus on repeatability, automation, and risk-controlled delivery.
• Strong collaboration across engineering, platform, and architecture stakeholders.
• Clear incident response behavior and disciplined root-cause analysis.
Success Measures (Examples):
• Reliable and repeatable deployments with low failure rates and controlled environment promotion.
• Reduced deployment cycle time without compromising governance, security, or stability.
• Production systems supported by monitoring, alerts, and clear runbooks.
• Fast recovery from incidents through rollback readiness and disciplined troubleshooting.
• Improved release confidence through automated validation and standardized deployment practices.
• Demonstrated ability to execute rapid, low-risk rollbacks of AI services when required (for example in response to security concerns such as prompt-injection exposure, or performance degradation), supporting low mean time to recovery (MTTR).
Workplace:
Salary:
Compensation & Other Benefits:
The Application Process:
N.B.: Only shortlisted candidates will be communicated in the recruitment process.