مهندس ذكاء اصطناعي وتعلم آلي

About the Role

Design, deploy, and maintain scalable AI and machine learning systems that deliver secure, reliable, and high-performing AI solutions. Ensure efficient model serving, deployment, monitoring, and operational excellence across AI environments.

Key Responsibilities

· Design, deploy, and maintain scalable AI/ML systems and infrastructure.

· Develop and manage MLOps pipelines for automated model deployment and monitoring.

· Ensure the performance, reliability, security, and scalability of AI platforms.

· Deploy, serve, and optimize machine learning and generative AI models for production environments.

· Build and maintain CI/CD pipelines for AI applications.

· Manage containerized AI applications using Docker and Kubernetes.

· Collaborate with data scientists, software engineers, and business stakeholders to operationalize AI solutions.

· Monitor AI system performance, reliability, and availability, implementing continuous improvements.

· Troubleshoot production issues and optimize AI infrastructure.

Requirements

· Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.

· 3–8 years of experience in AI systems engineering, MLOps, or machine learning platform engineering.

· Strong programming skills in Python.

· Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform.

· Hands-on experience with Docker, Kubernetes, and containerized deployments.

· Experience designing and maintaining CI/CD pipelines.

· Knowledge of distributed systems and scalable AI infrastructure.

· Experience deploying and operationalizing machine learning and generative AI solutions.

Preferred Qualifications

· Experience with Azure Machine Learning, AWS SageMaker, or Google Vertex AI.

· Experience with Infrastructure as Code (Terraform or similar).

· Familiarity with AI monitoring, observability, and model lifecycle management.

· Relevant cloud or AI certifications.