MLOps Engineer
Job Description & How to Apply Below
Job Code: 6315
Job Title:
Dev Ops / MLOps Engineer (AI & LLM Platforms)
Location:
Abu Dhabi
Contract:
1 year and renewable
Experience:
7+ years
The Dev Ops / 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 Dev Ops, Git Hub 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 Dev Ops/MLOps Engineers, AI Engineers, and Development teams.
- Support developers in environment setup, debugging, and deployment processes.
- Follow Dev Ops and MLOps best practices and continuously improve operational workflows.
Skills & Qualifications
- Bachelor s degree in Computer Science, Engineering, or related field.
- 7+ years of experience in Dev Ops 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 Dev Ops, Git Hub 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.
- 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.
- 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.
- Reports to:
Senior Dev Ops / MLOps Engineer / Platform Lead - Key Stakeholders:
- AI Engineers & Data Scientists
- Backend & Frontend Developers
- Dev Ops / Platform Team
- QA & Release Management Teams
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