AI Ops Engineer

Dicetek LLC; Abu Dhabi; On-site; AED 280,000 - 460,000; Full time

Job summary

Dicetek LLC in the United Arab Emirates seeks an experienced Production Engineer to advance cloud-native AI and high-scale API platforms in enterprise environments. The role emphasizes CI/CD pipelines, environment management, and strong release governance to ensure reliability.

You will work with LLMOps, AI lifecycle tooling, and observability practices, establishing reusable release standards and self-service workflows across engineering teams, while optimizing capacity and platform performance.

Qualifications

• Strong production engineering experience with cloud-native, AI or high-scale API platforms in enterprise environments.
• Hands-on experience with CI/CD, GitHub Actions or equivalent automation, and environment management.
• Working knowledge of LLMOPs, AI lifecycle management and release governance.
• Experience with progressive delivery, controlled rollout, canary releases and rollback readiness.
• Strong experience with observability, telemetry, dashboards, runbooks and production-readiness practices.
• Exposure to AI FinOps, token/model usage, platform capacity, quota management and cost optimization.
• Ability to establish reusable release standards, operational playbooks and self-service workflows across engineering teams.

Skills

Cloud-native platforms

CI/CD

LLMOps

Observability

Release governance

Cost optimization

Self-service workflows

AI lifecycle

Job description

Core Skills & Experience

• Strong production engineering experience with cloud-native, AI or high-scale API platforms in enterprise environments
• Hands-on experience with CI/CD, GitHub Actions or equivalent automation, and environment management
• Working knowledge of LLMOPs, AI lifecycle management and release governance
• Experience with progressive delivery, controlled rollout, canary releases and rollback readiness
• Strong experience with observability, telemetry, dashboards, runbooks and production-readiness practices
• Exposure to AI FinOps, token/model usage, platform capacity, quota management and cost optimization
• Ability to establish reusable release standards, operational playbooks and self-service workflows across engineering teams