AI Platform Engineer

Deeplight; United Arab Emirates; On-site; AED 120,000 - 200,000; Full time

Job summary

A leading AI consultancy in the United Arab Emirates is seeking an AI Platform Engineer to design and manage on-premises AI infrastructure. The role involves Kubernetes management, developing AI/ML pipelines, and collaborating with teams to optimize performance and reliability. Ideal candidates will have extensive experience in Kubernetes, infrastructure automation, and support for AI/ML workloads. This position offers competitive salary and growth opportunities.

Benefits

Competitive salary and performance bonuses

Comprehensive health insurance

Professional development and certification support

Opportunity to work on cutting-edge AI projects

International exposure and travel opportunities

Flexible working arrangements

Career advancement opportunities

Qualifications

• Proven experience managing Kubernetes clusters on-premises.
• Experience working in a Consultancy capacity.
• Hands-on experience with infrastructure-as-code tools.

Responsibilities

• Design, deploy, and manage on-premises Kubernetes clusters.
• Build and optimise AI/ML pipelines and MLOps workflows.
• Collaborate with data science and engineering teams.

Skills

Kubernetes management

Container orchestration

CI/CD

Linux administration

Docker

Networking fundamentals

Infrastructure as code

Scripting

DevOps best practices

MLOps best practices

Tools

Terraform

Helm

Ansible

TensorFlow

PyTorch

Hugging Face

Ray

Kubeflow

Prometheus

Grafana

ELK

Job description

DeepLight AI is a specialist AI and data consultancy with extensive experience implementing intelligent enterprise systems across multiple industries, with particular depth in financial services and banking. Our team combines deep expertise in data science, statistical modeling, AI/ML technologies, workflow automation, and systems integration with a practical understanding of complex business operations. The AI Platform Engineer helps design, build and maintain the on‑premises AI Infrastructure for our client, a market leader in the environment sector. The engineer plays a key role in enabling large‑scale machine learning and generative AI workloads by developing robust, scalable, and secure platform solutions that support data scientists and ML engineers across the organisation. The role requires deep Kubernetes and infrastructure automation experience to optimise performance, streamline deployments, and ensure reliability in an on‑prem environment. This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients.

Benefits & Growth Opportunities

• Competitive salary and performance bonuses
• Comprehensive health insurance
• Professional development and certification support
• Opportunity to work on cutting‑edge AI projects
• International exposure and travel opportunities
• Flexible working arrangements
• Career advancement opportunities in a rapidly growing AI company

Responsibilities

• Design, deploy, and manage on‑premises Kubernetes clusters for AI and ML workloads.
• Develop and maintain infrastructure‑as‑code using tools like Terraform, Helm, or Ansible.
• Build and optimise AI/ML pipelines and MLOps workflows for model training, deployment, and monitoring.
• Collaborate with data science and engineering teams to deliver high-performance computing environments for large model training and inference.
• Implement resource management, observability, and scaling strategies for GPU‑based workloads.
• Manage containerisation, networking, and storage solutions for AI workloads.
• Ensure security, compliance, and reliability of the AI platform.
• Automate operational processes and continuously improve platform efficiency.

Qualifications

• Proven experience managing Kubernetes clusters on‑premises (not just cloud‑managed solutions).
• Experience working in a Consultancy capacity.
• Strong background in container orchestration, CI/CD, and automation.
• Proficiency in Linux administration, Docker, and networking fundamentals.
• Hands‑on experience with infrastructure‑as‑code (Terraform, Helm, Ansible, etc.).
• Experience supporting AI/ML workloads (e.g., TensorFlow, PyTorch, Hugging Face, Ray, Kubeflow).
• Familiarity with GPU scheduling and resource management on Kubernetes.
• Knowledge of monitoring and logging tools (Prometheus, Grafana, ELK, etc.).
• Strong scripting skills (Python, Bash, or Go).
• Understanding of DevOps and MLOps best practices.