MLOps Engineer

Sundus; Abu Dhabi; On-site; AED 280,000 - 440,000; Full time

Job summary

Sundus in Abu Dhabi seeks a DevOps / MLOps Engineer to design, automate, and maintain infrastructure for AI/LLM workloads. You will build deployment pipelines, manage GPU-enabled environments, and ensure reliability across dev, test, and prod.

With 7+ years of hands-on DevOps/platform experience, you will work closely with AI engineers to operationalize ML models, deploy containers via Kubernetes, and implement robust monitoring and CI/CD practices. This role is based in Abu Dhabi.

Qualifications

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

Responsibilities

• Set up and maintain compute infrastructure, including GPU-enabled environments.
• Configure and manage Linux-based development and production environments.
• Provisioning and configuration of cloud and on-prem infrastructure.
• Monitor system resources and assist in performance tuning and optimization.
• Build and manage containerized applications using Docker.
• Deploy and manage applications on Kubernetes clusters with guidance.
• Create deployment configurations, Helm charts, and environment setups.
• Support scaling and orchestration of microservices and AI workloads.
• Develop and maintain CI/CD pipelines with Azure DevOps, GitHub Actions or Jenkins.
• Ensure smooth promotion of code and models across environments (dev, test, prod).
• Troubleshoot pipeline failures and deployment issues.
• Deploy ML 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.
• Implement and maintain monitoring and logging solutions (Prometheus, Grafana, ELK).
• Track application performance, system health and availability.
• Respond to incidents and perform root-cause analysis for continuous improvement.
• Write scripts (Python, Bash) to automate tasks and support IaC with Terraform/ARM.

Skills

Docker

Kubernetes

CI/CD

Linux admin

Python scripting

Git

Cloud basics

GitHub Actions

Jenkins

Azure DevOps

Education

Bachelor's degree in CS/Engineering

Tools

Terraform

ARM templates

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

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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