MLOps Engineer

SUNDUS MANAGEMENT CONSULTANCY & STUDIES BUREAUL.L.C; Abu Dhabi; On-site; AED 280,000 - 420,000; Full time

Job summary

SUNDUS MANAGEMENT CONSULTANCY & STUDIES BUREAUL.L.C seeks an experienced DevOps/Platform Engineer to design and maintain infrastructure. You will manage compute, Linux environments, and cloud/on-prem resources, enabling robust development and AI workloads.

The role emphasizes Docker/Kubernetes, CI/CD pipelines, and collaboration with AI engineers to operationalize models and ensure system reliability across environments.

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).
• Exposure to AI/ML model deployment and MLOps practices.

Responsibilities

• 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.

Skills

Linux administration

Python scripting

Bash scripting

Git (version control)

Microservices architecture

Education

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

Tools

Docker

Kubernetes

Azure DevOps

GitHub Actions

Jenkins

Terraform

ARM templates

Prometheus

Grafana

ELK

Job description

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 escalat 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.

• 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