AI Platform Release Engineer: Safe & Scalable AI Ops

Showcify, Inc.; Abu Dhabi; On-site; AED 180,000 - 260,000; Full time

Job summary

Showcify, Inc. seeks an AI Ops Engineer to establish the operational backbone for enterprise AI platforms in Abu Dhabi, enabling teams to release AI products safely, repeatedly, and at scale.

You will own production release discipline, LLMOps practices, deployment automation, governance, and cost visibility. Responsibilities include building AI release pipelines, implementing progressive delivery, embedding observability and readiness criteria, and providing self-service playbooks that balance

Qualifications

• Strong production engineering background in cloud-native AI or high-scale API platforms.
• Hands-on experience with CI/CD, GitHub Actions or equivalent automation.
• Working knowledge of LLMOps, telemetry, release governance, and production-readiness practices.
• Experience with observability, change control, service reliability, and continuous improvement.

Responsibilities

• AI Release Engineering & CI/CD: design standard release pipelines and promotion controls across environments.
• LLMOps & AI lifecycle management: embed operating controls for models and AI assets.
• Progressive Delivery: implement controlled rollout, canary releases, and rollback readiness.
• Evaluation, Observability & Production Readiness: embed AI quality checks, dashboards, runbooks, and readiness criteria.
• AI FinOps & Capacity Governance: provide visibility into model usage, token spend, and capacity.
• Self-Service & Continuous Improvement: create reusable templates, playbooks, and paved-road workflows.

Skills

CI/CD

GitHub Actions

LLMOps

Production engineering

Observability

Release governance

Cloud-native

Tools

GitHub Actions

Job description

Showcify, Inc. seeks an AI Ops Engineer to establish the operational backbone for enterprise AI platforms in Abu Dhabi, enabling teams to release AI products safely, repeatedly, and at scale.

You will own production release discipline, LLMOps practices, deployment automation, governance, and cost visibility. Responsibilities include building AI release pipelines, implementing progressive delivery, embedding observability and readiness criteria, and providing self-service playbooks that balance