Abu Dhabi, United Arab Emirates | Posted on 08/20/2026
We are looking for an experienced AIOps / MLOps Engineer to design, build, and maintain the infrastructure, tools, and processes that ensure the reliability, scalability, performance, and security of AI/ML systems throughout their lifecycle. You will work closely with Data Scientists, Data Engineers, and Software Engineers to streamline the development, deployment, and monitoring of both traditional and generative AI models, ensuring seamless integration into enterprise applications and services.
Key Responsibilities:
- Design and implement robust MLOps pipelines for the end-to-end lifecycle of AI/ML models, from experimentation and training to deployment, monitoring, and governance.
- Develop and maintain AI/ML infrastructure leveraging cloud-native technologies (primarily Azure) and open-source tools, including compute, storage, and networking optimized for AI/ML workloads.
- Build and manage CI/CD pipelines for AI/ML models and related code, automating testing, validation, and deployment processes.
- Implement monitoring and observability solutions for AI/ML systems, tracking model performance, data drift, infrastructure health, and application logs.
- Develop and integrate AIOps capabilities to automate incident detection, root cause analysis, and remediation for AI/ML infrastructure and applications.
- Establish and enforce MLOps best practices, including version control, experiment tracking (e.g., MLflow), model registry, deployment strategies (e.g., A/B testing, canary deployments), and security protocols.
- Collaborate with Data Scientists, AI Engineers, and Data Engineers to provide tools and infrastructure that accelerate research and development.
- Work with Software Engineers to integrate AI/ML models into applications and services, ensuring scalability, reliability, and performance.
- Implement and manage data governance and lineage solutions for AI/ML datasets and models, ensuring data quality, compliance, and auditability.
- Automate infrastructure provisioning and management using Infrastructure-as-Code (IaC) tools (e.g., Terraform, ARM templates).
- Evaluate and adopt new AIOps/MLOps tools and technologies to continuously improve AI/ML platforms and processes.
- Troubleshoot and resolve issues related to AI/ML infrastructure, pipelines, and deployments in production environments.
- Document all aspects of AI/ML infrastructure and MLOps processes clearly for both technical and non-technical stakeholders.
Required Skills & Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field.
- 5+ years of hands‑on experience in building and managing infrastructure and pipelines for ML applications in production.
- Strong understanding of the AI/ML lifecycle and challenges of deploying and maintaining AI systems at scale.
- Proven experience with cloud platforms, especially Microsoft Azure, and their AI/ML services (e.g., Azure Machine Learning, Azure Kubernetes Service, Azure Data Factory).
- Extensive experience with containerization (Docker) and orchestration frameworks (Kubernetes).
- Strong scripting and automation skills using Python, Bash, or PowerShell.
- Experience with CI/CD tools (Azure DevOps, Jenkins, GitLab CI) and Infrastructure-as-Code (Terraform, ARM templates).
- Experience with monitoring and observability tools (Azure Monitor, Prometheus, Grafana, ELK stack).
- Familiarity with MLOps platforms and tools (MLflow, Kubeflow).
- Knowledge of data governance and security best practices in a cloud environment.
- Excellent problem‑solving and troubleshooting skills with a systematic approach.
- Strong collaboration and communication skills.
- Proactive, automation‑first mindset with a passion for building reliable and efficient AI systems.
- Familiarity with AIOps concepts and tools for intelligent incident management and automation.
Preferred Qualifications / Bonus Points:
- Experience with AIOps platforms or tools.
- Experience deploying and managing generative AI models in production.
- Knowledge of security best practices for AI/ML systems.
- Experience with performance tuning and optimization of AI/ML infrastructure and pipelines.
- Certifications in relevant cloud platforms or DevOps/MLOps technologies.
- Experience with data lineage and data quality tools.
What We Offer:
- Competitive salary and benefits.
- Opportunity to work with cutting‑edge AI/ML technologies and cloud platforms.
- Dynamic, collaborative, and innovative work environment.
- Chance to lead strategic AI/ML infrastructure initiatives.
Requirements
Technical Skills:
- Strong understanding of the AI/ML lifecycle and challenges in deploying and maintaining AI systems at scale.
- Proven experience with cloud platforms, primarily Microsoft Azure, including Azure Machine Learning, Azure Kubernetes Service, and Azure Data Factory.
- Extensive experience with containerization and orchestration technologies (Docker, Kubernetes).
- Strong scripting and automation skills using Python, Bash, or PowerShell.
- Hands‑on experience with CI/CD pipelines (Azure DevOps, Jenkins, GitLab CI) and Infrastructure-as-Code tools (Terraform, ARM templates).
- Familiarity with monitoring and observability tools (Azure Monitor, Prometheus, Grafana, ELK stack).
- Knowledge of MLOps platforms and tools (MLflow, Kubeflow).
- Understanding of data governance, lineage, and security best practices in cloud environments.
- Experience in automating ML/AI workflows, managing model deployment, and ensuring reproducibility.
Soft Skills:
- Excellent problem‑solving, troubleshooting, and analytical skills.
- Strong collaboration and communication skills to work effectively with Data Scientists, AI Engineers, Data Engineers, and Software Engineers.
- Proactive and automation‑first mindset with a focus on reliability and efficiency.
Preferred / Bonus Skills:
- Experience with AIOps platforms or tools.
- Experience deploying and managing generative AI models in production.
- Knowledge of security best practices for AI/ML systems.
- Experience in performance tuning and optimization of AI/ML infrastructure and pipelines.
- Certifications in relevant cloud platforms or DevOps/MLOps technologies.