AI Ops Engineer

TalentRecruit Software Private Limited; Abu Dhabi; On-site; AED 300,000 - 600,000; Part time

Job summary

TalentRecruit Software Private Limited in Abu Dhabi is seeking an AI Ops Engineer to establish the operational backbone for enterprise AI platforms. The role focuses on production release discipline, LLMOps, deployment automation, governance, cost visibility, and self-service standards for AI-native delivery teams.

The role requires strong production engineering background with cloud-native AI platforms, hands-on CI/CD and environment management, and collaboration across platform engineering,

Qualifications

• Strong production engineering background in cloud-native AI or high-scale API platforms.
• Hands-on with CI/CD, GitHub Actions, and environment management.
• Experience with LLMOps, observability, and production-readiness practices.
• Ability to collaborate with platform engineering, QA/SRE, cybersecurity, architecture, product, and delivery teams.

Responsibilities

• AI Release Engineering & CI/CD: design release pipelines with controls across environments.
• LLMOps & AI Lifecycle Management: govern AI models and assets.
• Progressive Delivery: implement canary releases and controlled rollouts.
• Observability & Production Readiness: embed telemetry, dashboards, and runbooks.
• AI FinOps & Capacity Governance: monitor model usage and cost controls.
• Self-Service & Continuous Improvement: create templates and playbooks for teams.

Skills

Production engineering

Cloud-native

AI platforms

Enterprise environment

CI/CD

GitHub Actions

Observability

Reliability

Tools

GitHub Actions

CI/CD tooling

Job description

Job Location Abu Dhabi (Work From Office)

Job Type Contract

Job Description

Role Purpose

We are seeking 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. The role is accountable 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 Engineering & CI/CD: build and design standard release pipelines and promotion controls for AI applications, agents, platform services, and configuration changes across environments, ensuring repeatable deployment, governance, and release evidence.
• LLMOps & AI Lifecycle Management: embed operating controls for models, AI assetsand release for auditable.
• Progressive Delivery: Implement deployment patterns to reduce production risk, including controlled rollout, canary release, and rollback readiness
• Evaluation, Observability & Production Readiness: Embed AI quality checks, operational telemetry, dashboards, runbooks, and readiness criteria into the delivery lifecycle so AI services are measurable and supportable.
• AI FinOps & Capacity Governance: Provide visibility and controls for AI workload consumption, including model usage, token spend, platform capacity, quota management, and optimization opportunities.
• Self-Service & Continuous Improvement: Convert proven operating patterns into reusable templates, release standards, onboarding guidance, operational playbooks, and paved-road workflows that allow teams to move quickly while maintaining enterprise control.

Required Experience

• Strong production engineering background operating cloud-native, AI, or high-scale API platforms in an enterprise environment.
• Hands-on experience with CI/CD, GitHub Actions or equivalent automation, and environment management.
• Working knowledge of LLMOps, telemetry, release governance, and production-readiness practices.
• Experience with observability, change control, service reliability, and continuous operational improvement.
• Ability to partner with platform engineering, QA/SRE, cybersecurity, architecture, product, and delivery teams to standardize safe and scalable AI operations.