AI/ML Ops Engineer — Relocation to UAE, Production Pipelines

Tech Junction Ltd; United Arab Emirates; On-site; AED 260,000 - 420,000; Full time

Job summary

Talent Arabia in the UAE is seeking an experienced AI Ops Engineer to own operationalizing ML models, building automated deployment pipelines, and ensuring robust observability for production AI workloads. You will collaborate with data scientists, ML engineers, and cloud platform squads to guarantee resilience and low-latency inference.

Ideal candidates have a strong MSc/PhD-equivalent background in CS/AI, hands-on MLOps experience, and immediate availability.

Qualifications

• Bachelor’s or Master’s degree in CS/AI/Data Eng or related.
• 4+ years software engineering, with 2+ years in AI/ML ops in production.
• Proficiency with MLOps tools and orchestration (MLflow, Kubeflow, Airflow).
• Experience deploying AI workloads on AWS/Azure using Docker & Kubernetes.

Responsibilities

• Design, build, and maintain robust MLOps pipelines for production ML workloads.
• Manage end-to-end model deployment lifecycles, registries, and automated inference serving infrastructure.
• Configure monitoring, logging, and observability for AI models and data pipelines.
• Optimize model inference performance, GPU utilization, latency, and throughput in cloud environments.
• Implement CI/CD workflows for data science and AI deliverables.
• Collaborate with data scientists, ML engineers, and cloud architects.
• Ensure data privacy, governance standards, and security protocols.
• Maintain technical documentation, runbooks, and pipeline diagrams.
• Stay updated with emerging MLOps tools to improve infrastructure.

Skills

MLOps tools

Kubeflow

Airflow

Docker

Kubernetes

Python

Cloud platforms (AWS/Azure)

Education

Bachelor’s or Master’s degree in Computer Science, AI, Data Engineering or related

Tools

MLflow

Kubeflow

Airflow

Docker

Kubernetes

Triton Inference Server

Pinecone

Milvus

vLLM

Job description

Talent Arabia in the UAE is seeking an experienced AI Ops Engineer to own operationalizing ML models, building automated deployment pipelines, and ensuring robust observability for production AI workloads. You will collaborate with data scientists, ML engineers, and cloud platform squads to guarantee resilience and low-latency inference.

Ideal candidates have a strong MSc/PhD-equivalent background in CS/AI, hands-on MLOps experience, and immediate availability.