AI Ops Engineer

D4Insight; Abu Dhabi; On-site; AED 260,000 - 460,000; Full time

Job summary

D4Insight in Abu Dhabi, United Arab Emirates, seeks an AI Ops Engineer to establish the operational backbone for enterprise AI platforms, enabling application teams to release AI products safely, repeatedly, and at scale.

You will lead production release discipline, LLMOps practices, deployment automation, governance, cost visibility, and self-service operating standards for AI-native delivery teams as part of a global platform engineering effort.

Qualifications

• 7+ years of production engineering experience in enterprise environments.
• Hands-on CI/CD and GitHub Actions or equivalent automation.
• Experience with LLMops and AI platform deployments.
• Strong collaboration with Platform Eng, QA/SRE, Cybersecurity, Architecture, Product.

Responsibilities

• Build standard release pipelines and promotion controls for AI apps, agents, platform services and configuration changes.
• Ensure repeatable deployments with governance and release evidence.
• Implement controlled rollout, canary release, and rollback-readiness practices to reduce production risk.
• Embed operating controls for models and AI assets to support auditability and AI lifecycle management.
• Provide visibility into AI workload consumption and platform capacity.

Skills

LLMOps

CI/CD

GitHub Actions

Release governance

Observability

Production readiness

Platform collaboration

Cost visibility

Tools

CI/CD pipelines

Job description

Role Overview:

We are seeking for AI Ops Engineer to establish the operational backbone for enterprise AI platforms, enabling application teams to release AI products safely, repeatedly, and at scale.

The role is responsible for production release discipline, LLMOps practices, deployment automation, operational governance, cost visibility, and self-service operating standards for AI-native delivery teams.

Key Responsibilities:

AI Release & Deployment

• Build standard release pipelines and promotion controls for AI applications, agents, platform services, and configuration changes.
• Ensure repeatable deployments with appropriate governance and release evidence.
• Implement controlled rollout, canary release, and rollback-readiness practices to reduce production risk.

LLMOps & Operational Governance

• Embed operating controls for models and AI assets to support auditability and AI lifecycle management.
• Integrate AI quality checks, operational telemetry, dashboards, runbooks, and production-readiness criteria into delivery processes.
• Establish reusable operational standards and practices for AI-native teams.

Cost & Platform Visibility

• Provide visibility into AI workload consumption, including:
  • Model usage
  • Token spend
  • Platform capacity
  • Quota management
  • Optimization opportunities
• Convert proven operating patterns into reusable templates, release standards, onboarding guidance, and operational playbooks.

Required Skills & Experience:

• 7+ years of relevant experience.
• Strong production engineering background 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
  • Telemetry
  • Release governance
  • Production readiness
• Experience with observability, change control, service reliability, and continuous operational improvement.
• Ability to collaborate with Platform Engineering, QA/SRE, Cybersecurity, Architecture, Product, and Delivery teams.

We craft, deploy, and manage bespoke services in CRM, data and AI, cybersecurity and consulting.