Senior AI Platform Engineer - MLOps & Generative AI

Quantum Talent Group; Abu Dhabi; On-site; AED 450,000 - 750,000; Full time

Job summary

Quantum Talent Group is seeking a highly experienced Senior AI Solutions Engineer to design, architect, and deliver enterprise AI platforms for AI, ML, and Generative workloads in Abu Dhabi. This customer-facing role blends AI infrastructure expertise with cloud-native platform engineering, Kubernetes administration, and GPU-enabled solutions.

You will collaborate with customers, data scientists, and engineering teams to implement scalable architectures for LLM serving, RAG workflows, and

Qualifications

• Bachelor’s degree in CS/IT/Engineering or related field.
• 8+ years in Infrastructure/Platform/DevOps.
• 2+ years supporting AI/ML workloads or GPU computing.
• Strong production Kubernetes/OpenShift deployments.

Responsibilities

• Design and deliver enterprise AI infrastructure platforms for ML and Generative AI workloads.
• Architect scalable Kubernetes/OpenShift environments for AI, MLOps, and GPU apps.
• Lead customer workshops and architecture discussions; translate requirements into technical designs.
• Develop Python automation and IaC solutions; ensure high availability and security.
• Support GPU resource allocation, scheduling, and cluster optimization.
• Collaborate with AI engineers, data scientists, cloud architects, and DevOps teams.

Skills

Kubernetes

OpenShift

Python scripting

Terraform

Ansible

GitOps

LLM serving

RAG design

GPU compute

Solution architecture

Education

Bachelor's Degree in CS/IT/Engineering

Tools

CUDA

Docker

Terraform

Ansible

GitOps tooling

Job description

Quantum Talent Group is seeking a highly experienced Senior AI Solutions Engineer to design, architect, and deliver enterprise AI platforms for AI, ML, and Generative workloads in Abu Dhabi. This customer-facing role blends AI infrastructure expertise with cloud-native platform engineering, Kubernetes administration, and GPU-enabled solutions.

You will collaborate with customers, data scientists, and engineering teams to implement scalable architectures for LLM serving, RAG workflows, and