AI Cloud Engineer

Key Responsibilities:

• Design, implement, and manage AI/ML models and workflows on cloud platforms such as AWS, Azure, or Google Cloud.
• Develop and maintain scalable data pipelines to ingest, process, and analyze large datasets for AI applications.
• Collaborate with data scientists, ML engineers, and software developers to deploy AI models into production environments.
• Automate infrastructure provisioning and deployment using Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Azure Resource Manager.
• Monitor AI system performance, optimize resource usage, and ensure high availability and security.
• Implement best practices for cloud security, data governance, and compliance in AI projects.
• Troubleshoot issues related to cloud infrastructure, AI workloads, and model deployment.
• Stay updated with the latest trends and technologies in AI, ML, and cloud computing.
• Provide documentation and training to stakeholders on AI cloud solutions and usage.

Qualifications and Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
• 3+ years of experience in cloud engineering with hands-on AI/ML deployment experience.
• Proficiency with cloud platforms like AWS (SageMaker), Azure (ML Studio), or Google Cloud (AI Platform).
• Strong programming skills in Python, R, or Java, especially for AI/ML workflows.
• Experience with containerization and orchestration tools such as Docker and Kubernetes.
• Familiarity with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
• Knowledge of data storage, databases, and big data technologies on cloud platforms.
• Understanding of DevOps practices and CI/CD pipelines for AI model deployment.
• Strong problem-solving and collaboration skills.