AIOps/MLOps Engineer
Listed on 2025-12-02
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IT/Tech
AI Engineer, Machine Learning/ ML Engineer, Cloud Computing, Data Engineer
Abu Dhabi, United Arab Emirates | Posted on 08/21/2025
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 Power Shell
.
Experience with
CI/CD tools (Azure Dev Ops, Jenkins, Git Lab 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…
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