MLOps Engineer

Sundus Gulf; Abu Dhabi; On-site; AED 320,000 - 520,000; Full time

Job summary

Sundus Gulf in Abu Dhabi is seeking a seasoned DevOps / MLOps Engineer to design, automate, and maintain AI/ML infrastructure and deployment pipelines, including LLM workloads. The role emphasizes reliability, scalability, and performance across environments.

The candidate will drive containerization, CI/CD, MLOps integration, monitoring, and collaboration with AI and development teams to deliver robust production systems.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.

Responsibilities

• Infrastructure Support & Environment Management: Set up and maintain compute infrastructure, including GPU-enabled environments; Configure and manage Linux-based systems; Provisioning and configuration of cloud and on-prem infrastructure; Monitor resources and assist in performance tuning and optimization.
• Containerization & Deployment: Build and manage containerized applications using Docker; Deploy and manage applications on Kubernetes clusters; Create deployment configurations, Helm charts, and environment setups; Support scaling of microservices and AI workloads.
• CI/CD Pipeline Implementation: Develop and maintain CI/CD pipelines for application and AI model deployment; Automate build, test, and deployment processes using Azure DevOps, GitHub Actions, or Jenkins; Promote code and models across environments; Troubleshoot pipeline failures.
• MLOps & AI Deployment Support: Deploy machine learning models and LLM-based services; Integrate AI components into production systems; Contribute to model versioning, monitoring, and lifecycle management; Work with AI engineers to operationalize RAG pipelines and inference services.
• Monitoring, Logging & Issue Resolution: Implement monitoring and logging solutions (Prometheus, Grafana, ELK); Track performance and availability; Respond to incidents; Perform root cause analysis and continuous improvement.
• Automation & Scripting: Write Python/Bash scripts to automate tasks; Support IaC initiatives using Terraform or ARM templates; Improve efficiency through automation.
• Collaboration & Support: Work with Senior DevOps/MLOps Engineers, AI Engineers, and Development teams; Support developers in environment setup and deployment; Follow DevOps/MLOps best practices.

Skills

7+ years experience

Linux system administration

Python scripting

Bash scripting

Git

Docker

Kubernetes

CI/CD tools

Cloud platforms

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Docker

Kubernetes

Terraform

ARM templates

Azure DevOps

GitHub Actions

Jenkins

Job description

Job Code: 6315

Job Title: DevOps / MLOps Engineer (AI & LLM Platforms)

Location: Abu Dhabi

Contract: 1 year and renewable

Experience: 7+ years

Role Purpose

The DevOps / MLOps Engineer is responsible for the setup, automation, and maintenance of infrastructure and deployment pipelines for AI/ML and microservices-based applications. This role focuses on enabling efficient development, testing, and deployment of AI solutions, including LLM workloads, while ensuring system reliability, scalability, and performance.

Key Responsibilities

1. Infrastructure Support & Environment Management

• Set up and maintain compute infrastructure, including GPU-enabled environments.
• Configure and manage Linux-based systems for development and production environments.
• Provisioning and configuration of cloud and on-prem infrastructure.
• Monitor system resources and assist in performance tuning and optimization.

2. Containerization & Deployment

• Build and manage containerized applications using Docker.
• Deploy and manage applications on Kubernetes clusters under guidance from senior engineers.
• Creating deployment configurations, Helm charts, and environment setups.
• Support scaling and orchestration of microservices and AI workloads.

3. CI/CD Pipeline Implementation

• Develop and maintain CI/CD pipelines for application and AI model deployment.
• Automate build, test, and deployment processes using tools like Azure DevOps, GitHub Actions, or Jenkins.
• Ensure smooth promotion of code and models across environments (dev, test, prod).
• Troubleshoot pipeline failures and deployment issues.

4. MLOps & AI Deployment Support

• Deploying machine learning models and LLM-based services.
• Integration of AI components into production systems.
• Contribute to model versioning, monitoring, and lifecycle management.
• Work with AI engineers to operationalize RAG pipelines and inference services.

5. Monitoring, Logging & Issue Resolution

• Implement and maintain monitoring and logging solutions (e.g., Prometheus, Grafana, ELK).
• Track application performance, system health, and availability.
• Respond to incidents, troubleshoot issues, and elevate when required.
• Assist in root cause analysis and continuous improvement.

6. Automation & Scripting

• Write scripts (Python, Bash) to automate repetitive operational tasks.
• Support Infrastructure as Code (IaC) initiatives using tools like Terraform or ARM templates.
• Improve operational efficiency through automation and tooling.

7. Collaboration & Support

• Work closely with Senior DevOps/MLOps Engineers, AI Engineers, and Development teams.
• Support developers in environment setup, debugging, and deployment processes.
• Follow DevOps and MLOps best practices and continuously improve operational workflows.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• 7+ years of experience in DevOps or platform engineering roles.
• Basic to intermediate experience with Linux system administration.
• Hands-on experience with Docker and containerization.
• Familiarity with Kubernetes (deployment and basic management).
• Experience with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins, etc.).
• Basic understanding of cloud platforms (Azure, AWS, or GCP).
• Scripting skills in Python, Bash, or similar.
• Understanding of version control systems (Git).

Preferred Skills

• Exposure to AI/ML model deployment and MLOps practices.
• Familiarity with LLM deployment concepts and tools.
• Basic knowledge of GPU environments and high-performance computing.
• Experience with monitoring and logging tools (Prometheus, Grafana, ELK).
• Knowledge of Infrastructure as Code (Terraform, ARM templates).
• Understanding of microservices architecture.

Key Performance Indicators (KPIs)

• Deployment success rate and pipeline stability.
• System uptime and availability.
• Resolution time for incidents and issues.
• Efficiency of CI/CD processes.
• Infrastructure utilization and basic cost optimization.
• Support effectiveness for development and AI teams.

Stakeholders & Reporting

• Reports to: Senior DevOps / MLOps Engineer / Platform Lead
• Key Stakeholders:
  • AI Engineers & Data Scientists
  • Backend & Frontend Developers
  • DevOps / Platform Team
  • QA & Release Management Teams