MLOps & AI Platform Engineer

• *Job Description: MLOps \& AI Platform Engineer**

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• *Job Title:** MLOps \& AI Platform Engineer

• *Experience:** 3–11 Years

• *Location:** **Riyadh \- Onsite**

• *Employment Type:** Full\-Time

• *Job Overview**
• ---------------

We are seeking a skilled **MLOps \& AI Platform Engineer** with **3–11 years of experience** to build, automate, and manage scalable machine learning platforms and production AI environments. The ideal candidate will have hands\-on expertise in MLOps, Kubernetes, cloud\-native AI infrastructure, CI/CD automation, and model lifecycle management. You will be responsible for enabling data scientists and AI engineers to efficiently develop, deploy, monitor, and maintain machine learning models at scale.

• *Key Responsibilities**
• -----------------------

• Design, build, and maintain enterprise\-grade MLOps platforms and AI infrastructure.
• Develop and automate end\-to\-end machine learning pipelines for training, validation, deployment, and monitoring.
• Implement model versioning, experiment tracking, and model registry solutions.
• Build scalable CI/CD pipelines for AI/ML workloads.
• Deploy and manage machine learning workloads on Kubernetes\-based environments.
• Collaborate with Data Scientists, AI Engineers, Data Engineers, and DevOps teams to operationalize ML solutions.
• Implement Infrastructure as Code (IaC) for cloud\-native AI platforms.
• Monitor platform health, model performance, and infrastructure availability.
• Ensure platform security, scalability, reliability, and operational excellence.
• Troubleshoot production issues and continuously optimize platform performance.

• *Required Technical Skills**
• ----------------------------

### **MLOps Platforms**

• Hands\-on experience with **Kubeflow or Vertex AI Pipelines or SageMaker Pipelines**.
• Strong experience with **MLflow** for experiment tracking, model registry, and lifecycle management.
• Experience orchestrating machine learning workflows using **Apache Airflow**.

### **Containerization \& Orchestration**

• Strong expertise in **Kubernetes (GKE or AKS or EKS)**.
• Experience deploying and managing containerized AI/ML workloads in cloud environments.

### **Infrastructure Automation**

• Hands\-on experience with **Terraform** for Infrastructure as Code (IaC).
• Experience automating infrastructure provisioning and cloud resource management.

### **CI/CD \& DevOps**

• Experience with **GitHub Actions** for CI/CD automation.
• Knowledge of DevOps best practices, Git workflows, and automated deployments.

### **Monitoring \& Observability**

• Experience using **Prometheus** for infrastructure and application monitoring.
• Knowledge of logging, alerting, and performance monitoring for AI platforms.

• *Qualifications**
• -----------------

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related field.
• **3–11 years** of professional experience in MLOps, DevOps, Platform Engineering, Cloud Engineering, or AI Infrastructure.
• Strong scripting and automation skills using Python, Bash, or similar languages.
• Excellent analytical and problem\-solving skills.
• Experience working in Agile/Scrum environments.

• *Preferred Skills**
• -------------------

• Experience with Docker and containerized application deployment.
• Knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Familiarity with model monitoring, drift detection, and automated retraining pipelines.
• Experience implementing security best practices for AI/ML platforms.
• Cloud and Kubernetes certifications are a plus.

• *Key Technology Stack**
• -----------------------

• **MLOps Platforms:** **Kubeflow or Vertex AI Pipelines or SageMaker Pipelines**
• **Workflow Orchestration:** **Apache Airflow** **and** MLflow
• **Container Orchestration:** **Kubernetes (GKE or AKS or EKS)**
• **Infrastructure as Code:** Terraform
• **CI/CD:** GitHub Actions
• **Monitoring:** Prometheus
• **Cloud Platforms:** **Google Cloud Platform or Microsoft Azure or Amazon Web Services** (Preferred)
• **Automation:** Python **and** Bash (Preferred)

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